Machine Learning Algorithms in Distributed Systems

• Effective and efficient training involving diverse data and models located at distributed computed nodes (e.g., edge devices/servers), including federated learning and beyond.
• Representative contributions: learning from arbitrarily participating clients [NeurIPS’22, ICLR’24], adaptive federated optimization algorithms [ICLR'23, ICML'24], vertical federated learning from features partitioned across parties [ICML'22, ICML'23, ICLR'25], one-shot model fusion [AISTATS’24], multi-level parameter updates in hierarchical systems [AAAI'22, NeurIPS'24], sample reweighting for LLM training [ICLR’25]

Optimizing AI Workloads

• Optimizing controllable parameters of AI algorithms and systems, subject to constraints related to system characteristics and practical requirements.
• Representative contributions: resource-aware control of federated learning [JSAC'19, INFOCOM'23], adaptive model pruning in edge computing systems [TNNLS'23], robust coreset construction for efficient data sharing [JSAC'20, MobiHoc'24], benchmarking federated learning on edge devices [MIDDLEWARE'24], optimizing energy-accuracy tradeoff in LLM inference [AFM@NeurIPS'24]

Placement and Scheduling in Edge Computing

• Online decision making in edge computing and related systems/applications, for problems including service placement and workload scheduling.
• Representative contributions: dynamic service migration based on Markov decision process [ToN'19], online decision making with inaccurate predictions [TPDS'17, NeurIPS'20], placement and scheduling with heterogeneous resource types [ICDCS'18, INFOCOM'19], live migration implementation [Wireless'18]

Industrial Applications of AI

• AI algorithms and systems for a wide range of practical applications, using various techniques including foundation models / LLMs, distributed MLOps [FLSys@MLSys'23 Talk], out-of-distribution detection [CVPR'23], classification of environmental sounds [DCASE'18 Challenge (winner of Task 5)], graph neural networks [AAAI'22], etc.

Research Impact

• Pioneering and foundational research contributions to edge computing and federated learning, with highly influential publications that inspired a lot of follow up work worldwide (partly reflected by 2,000+ citations of the adaptive federated learning paper, 10,000+ citations overall, according to Google Scholar. Received major awards including the IEEE Communications Society Leonard G. Abraham Prize in 2021.
• Open-source research code on federated learning and edge computing, available on GitHub. A single code repository received 250+ stars and has been widely used by research teams internationally.
• Influenced and integrated research outcomes into IBM products (e.g., watsonx, Maximo, Cloud Pak for Data) and community-scale open LLMs (Granite) & open-source AI-related projects (data-prep-kit) driven by IBM. Received multiple highly selective awards from IBM.

Note: Co-authors named before me who are students and early-stage postdoctoral researchers (<2 years after graduation), at the time of performing the work, are underlined with “        ” and “        ”, respectively.

Selected Recent Publications

• D. Sow, H. Woisetschläger, S. Bulusu, S. Wang, H.-A. Jacobsen, Y. Liang, "Dynamic loss-based sample reweighting for improved large language model pretraining," in International Conference on Learning Representations (ICLR), May 2025.
• P. Valdeira, S. Wang, Y. Chi, "Vertical federated learning with missing features during training and inference," in International Conference on Learning Representations (ICLR), May 2025.
• R. Zhang, H. Woisetschläger, S. Wang, H.-A. Jacobsen, "MESS+: energy-optimal inferencing in language model zoos with service level guarantees," in Workshop on Adaptive Foundation Models in Conjunction with NeurIPS 2024, Dec. 2024.
• W. Fang, D.-J. Han, E. Chen, S. Wang, C. Brinton, "Hierarchical federated learning with multi-timescale gradient correction," in the 38th Conference on Neural Information Processing Systems (NeurIPS), Dec. 2024 (acceptance rate: 25.8%).
• H. Woisetschläger, A. Erben, R. Mayer, S. Wang, H.-A. Jacobsen, "FLEdge: benchmarking federated machine learning applications in edge computing systems," in ACM/IFIP International Middleware Conference (MIDDLEWARE), Dec. 2024. [DOI] [Code]
• C.-C. Chiu, T. Nguyen, T. He, S. Wang, B.-S. Kim, K.-I. Kim, "Active learning for WBAN-based health monitoring," in ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (MobiHoc), Oct. 2024 (acceptance rate: 24.5%). [DOI]
• H. Woisetschläger, A. Erben, S. Wang, R. Mayer, H.-A. Jacobsen, "A survey on efficient federated learning methods for foundation model training," in International Joint Conference on Artificial Intelligence (IJCAI), Aug. 2024. [DOI]
• H. Woisetschläger, A. Erben, B. Marino, S. Wang, N. D. Lane, R. Mayer, H.-A. Jacobsen, "Federated learning priorities under the European Union Artificial Intelligence Act," in Workshop on Generative AI and Law (GenLaw) in Conjunction with ICML 2024, Jul. 2024.
• Y. Wang, S. Wang, S. Lu, J. Chen, "FADAS: towards federated adaptive asynchronous optimization," in International Conference on Machine Learning (ICML), Jul. 2024 (acceptance rate: 27.5%). [Proceedings Link] [Code]
• J. Wang, S. Wang, R.-R Chen, M. Ji, "A new theoretical perspective on data heterogeneity in federated optimization," in International Conference on Machine Learning (ICML), Jul. 2024 (acceptance rate: 27.5%). [Proceedings Link]
• S. Wang, M. Ji, "A lightweight method for tackling unknown participation statistics in federated averaging," in International Conference on Learning Representations (ICLR), May 2024 (spotlight, 5% of submitted papers). [Proceedings Link] [Code]
• D. Jhunjhunwala, S. Wang, G. Joshi, "FedFisher: leveraging Fisher information for one-shot federated learning," in International Conference on Artificial Intelligence and Statistics (AISTATS), May 2024. [Proceedings Link] [Code]
• Y. Jiang, S. Wang, V. Valls, B. J. Ko, W.-H. Lee, K. K. Leung, L. Tassiulas, "Model pruning enables efficient federated learning on edge devices," IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 12, pp. 10374 – 10386, Dec. 2023. [DOI] [Code]
• H. Wang, D. Joshi, S. Wang, Q. Ji, "Gradient-based uncertainty attribution for explainable Bayesian deep learning," in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2023 (acceptance rate: 25.8%). [DOI]
• D. Jhunjhunwala, S. Wang, G. Joshi, "FedExP: speeding up federated averaging via extrapolation," in International Conference on Learning Representations (ICLR), May 2023 (spotlight – notable-top-25%, top 25% of accepted papers, approximately top 8% of submitted papers). [Proceedings Link] [Code]
• S. Wang, J. Perazzone, M. Ji, K. Chan, "Federated learning with flexible control," in IEEE International Conference on Computer Communications (INFOCOM), May 2023 (acceptance rate: 19.2%). [DOI] [Code]
• S. Wang, M. Ji, "A unified analysis of federated learning with arbitrary client participation," in the 36th Conference on Neural Information Processing Systems (NeurIPS), Nov.–Dec. 2022 (acceptance rate: 25.6%). [Proceedings Link] [Code]
• T. Castiglia, A. Das, S. Wang, S. Patterson, "Compressed-VFL: communication-efficient learning with vertically partitioned data," in International Conference on Machine Learning (ICML), Jul. 2022 (acceptance rate: 21.9%). [Proceedings Link] [Code]
• A. Feng, C. You, S. Wang, L. Tassiulas, "KerGNNs: interpretable graph neural networks with graph kernels," in AAAI Conference on Artificial Intelligence, Feb.-Mar. 2022 (oral presentation, oral acceptance rate: 4.6%, overall acceptance rate: 15.0%). [DOI] [Code]
• H. Lu, M.-J. Li, T. He, S. Wang, V. Narayanan, K. Chan, "Robust Coreset Construction for Distributed Machine Learning," IEEE Journal on Selected Areas in Communications, vol. 38, no. 10, pp. 2400 – 2417, Oct. 2020 (acceptance rate of this special issue of the journal: 20.8%). [DOI] [Code]
• S. Wang, T. Tuor, T. Salonidis, K. K. Leung, C. Makaya, T. He, K. Chan, "Adaptive federated learning in resource constrained edge computing systems," IEEE Journal on Selected Areas in Communications, vol. 37, no. 6, pp. 1205 – 1221, Jun. 2019 (acceptance rate of this special issue of the journal: 13%, received the IEEE Communications Society Leonard G. Abraham Prize in 2021). [DOI] [Code]

All Publications

Shortcut to:  AI & ML Conferences  |  Networking & Systems Conferences  |  Journals & Magazines  |  Book Chapters  |  Other Conferences, Workshops & Short Papers  |  Patents  |  Thesis  |  Technical Reports

High-Quality AI & Machine Learning Conferences (full papers only, at least 9 pages including everything)

1. D. Sow, H. Woisetschläger, S. Bulusu, S. Wang, H.-A. Jacobsen, Y. Liang, "Dynamic loss-based sample reweighting for improved large language model pretraining," in International Conference on Learning Representations (ICLR), May 2025.
2. P. Valdeira, S. Wang, Y. Chi, "Vertical federated learning with missing features during training and inference," in International Conference on Learning Representations (ICLR), May 2025.
3. W. Fang, D.-J. Han, E. Chen, S. Wang, C. Brinton, "Hierarchical federated learning with multi-timescale gradient correction," in the 38th Conference on Neural Information Processing Systems (NeurIPS), Dec. 2024 (acceptance rate: 25.8%).
4. H. Woisetschläger, A. Erben, S. Wang, R. Mayer, H.-A. Jacobsen, "A survey on efficient federated learning methods for foundation model training," in International Joint Conference on Artificial Intelligence (IJCAI), Aug. 2024. [DOI]
5. Y. Wang, S. Wang, S. Lu, J. Chen, "FADAS: towards federated adaptive asynchronous optimization," in International Conference on Machine Learning (ICML), Jul. 2024 (acceptance rate: 27.5%). [Proceedings Link] [Code]
6. J. Wang, S. Wang, R.-R Chen, M. Ji, "A new theoretical perspective on data heterogeneity in federated optimization," in International Conference on Machine Learning (ICML), Jul. 2024 (acceptance rate: 27.5%). [Proceedings Link]
7. S. Wang, M. Ji, "A lightweight method for tackling unknown participation statistics in federated averaging," in International Conference on Learning Representations (ICLR), May 2024 (spotlight, 5% of submitted papers). [Proceedings Link] [Code]
8. D. Jhunjhunwala, S. Wang, G. Joshi, "FedFisher: leveraging Fisher information for one-shot federated learning," in International Conference on Artificial Intelligence and Statistics (AISTATS), May 2024. [Proceedings Link] [Code]
9. G. Xiong, G. Yan, S. Wang, J. Li, "DePRL: achieving linear convergence speedup in personalized decentralized learning with shared representations," in AAAI Conference on Artificial Intelligence, Feb. 2024 (acceptance rate: 23.7%). [DOI]
10. J. Park, D.-J. Han, J. Kim, S. Wang, C. Brinton, J. Moon, "StableFDG: style and attention based learning for federated domain generalization," in the 37th Conference on Neural Information Processing Systems (NeurIPS), Dec. 2023 (acceptance rate: 26.1%). [Proceedings Link]
11. T. Castiglia, Y. Zhou, S. Wang, S. Kadhe, N. Baracaldo, S. Patterson, "LESS-VFL: communication-efficient feature selection for vertical federated learning," in International Conference on Machine Learning (ICML), Jul. 2023 (acceptance rate: 27.9%). [Proceedings Link]
12. H. Wang, D. Joshi, S. Wang, Q. Ji, "Gradient-based uncertainty attribution for explainable Bayesian deep learning," in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2023 (acceptance rate: 25.8%). [DOI]
13. D. Jhunjhunwala, S. Wang, G. Joshi, "FedExP: speeding up federated averaging via extrapolation," in International Conference on Learning Representations (ICLR), May 2023 (spotlight – notable-top-25%, top 25% of accepted papers, approximately top 8% of submitted papers). [Proceedings Link] [Code]
14. S. Wang, M. Ji, "A unified analysis of federated learning with arbitrary client participation," in the 36th Conference on Neural Information Processing Systems (NeurIPS), Nov.–Dec. 2022 (acceptance rate: 25.6%). [Proceedings Link] [Code]
15. T. Castiglia, A. Das, S. Wang, S. Patterson, "Compressed-VFL: communication-efficient learning with vertically partitioned data," in International Conference on Machine Learning (ICML), Jul. 2022 (acceptance rate: 21.9%). [Proceedings Link] [Code]
16. J. Wang, S. Wang, R.-R. Chen, M. Ji, "Demystifying why local aggregation helps: convergence analysis of hierarchical SGD," in AAAI Conference on Artificial Intelligence, Feb.-Mar. 2022 (acceptance rate: 15.0%). [DOI]
17. A. Feng, C. You, S. Wang, L. Tassiulas, "KerGNNs: interpretable graph neural networks with graph kernels," in AAAI Conference on Artificial Intelligence, Feb.-Mar. 2022 (oral presentation, oral acceptance rate: 4.6%, overall acceptance rate: 15.0%). [DOI] [Code]
18. S. Pasteris, T. He, F. Vitale, S. Wang, M. Herbster, "Online learning of facility locations," in the 32nd International Conference on Algorithmic Learning Theory (ALT), Mar. 2021. [Proceedings Link]
19. S. Wang, J. Li, S. Wang, "Online algorithms for multi-shop ski rental with machine learned advice," in the 34th Conference on Neural Information Processing Systems (NeurIPS), Dec. 2020 (acceptance rate: 20.1%). [Proceedings Link] [Code]
20. S. Pasteris, F. Vitale, K. Chan, S. Wang, M. Herbster, "MaxHedge: maximising a maximum online," in International Conference on Artificial Intelligence and Statistics (AISTATS), Apr. 2019. [Proceedings Link]

High-Quality Networking & Systems Conferences (full papers only, at least 9 pages including everything)

1. H. Woisetschläger, A. Erben, R. Mayer, S. Wang, H.-A. Jacobsen, "FLEdge: benchmarking federated machine learning applications in edge computing systems," in ACM/IFIP International Middleware Conference (MIDDLEWARE), Dec. 2024. [DOI] [Code]
2. C.-C. Chiu, T. Nguyen, T. He, S. Wang, B.-S. Kim, K.-I. Kim, "Active learning for WBAN-based health monitoring," in ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (MobiHoc), Oct. 2024 (acceptance rate: 24.5%). [DOI]
3. E. Chen, S. Wang, C. Brinton, "Taming subnet-drift in D2D-enabled fog learning: a hierarchical gradient tracking approach," in IEEE International Conference on Computer Communications (INFOCOM), May 2024 (acceptance rate: 19.6%). [DOI]
4. P. Han, S. Wang, Y. Jiao, J. Huang, "Federated learning while providing model as a service: joint training and inference optimization," in IEEE International Conference on Computer Communications (INFOCOM), May 2024 (acceptance rate: 19.6%). [DOI]
5. B. Luo, Y. Feng, S. Wang, J. Huang, L. Tassiulas, "Incentive mechanism design for unbiased federated learning with randomized client participation," in IEEE International Conference on Distributed Computing Systems (ICDCS), Jul. 2023 (acceptance rate: 18.9%) [DOI].
6. S. Wang, J. Perazzone, M. Ji, K. Chan, "Federated learning with flexible control," in IEEE International Conference on Computer Communications (INFOCOM), May 2023 (acceptance rate: 19.2%). [DOI] [Code]
7. J. Perazzone, S. Wang, M. Ji, K. Chan, "Communication-efficient device scheduling for federated learning using stochastic optimization," in IEEE International Conference on Computer Communications (INFOCOM), May 2022 (acceptance rate: 19.9%). [DOI]
8. B. Luo, W. Xiao, S. Wang, J. Huang, L. Tassiulas, "Tackling system and statistical heterogeneity for federated learning with adaptive client sampling," in IEEE International Conference on Computer Communications (INFOCOM), May 2022 (acceptance rate: 19.9%). [DOI]
9. K. Yu, Q. Li, D. Chen, M. Rahman, S. Wang, "PrivacyGuard: enhancing smart home user privacy," in ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN), May 2021 (acceptance rate: 24.8%). [DOI] [Code]
10. B. Luo, X. Li, S. Wang, J. Huang, L. Tassiulas, "Cost-effective federated learning design," in IEEE International Conference on Computer Communications (INFOCOM), May 2021 (acceptance rate: 19.9%). [DOI]
11. Y. Han, S. Shen, X. Wang, S. Wang, V. C. M. Leung, "Tailored learning-based scheduling for kubernetes-oriented edge-cloud system," in IEEE International Conference on Computer Communications (INFOCOM), May 2021 (acceptance rate: 19.9%). [DOI] [Journal Version] [Code]
12. P. Han, S. Wang, K. K. Leung, "Adaptive gradient sparsification for efficient federated learning: an online learning approach," in IEEE International Conference on Distributed Computing Systems (ICDCS), Nov. 2020 (acceptance rate: 18.0%). [DOI] [Code]
13. H. Lu, T. He, S. Wang, C. Liu, M. Mahdavi, V. Narayanan, K. Chan, S. Pasteris, "Communication-efficient k-means for edge-based machine learning," in IEEE International Conference on Distributed Computing Systems (ICDCS), Nov. 2020 (acceptance rate: 18.0%). [DOI]
14. H. Lu, C. Liu, S. Wang, T. He, V. Narayanan, K. Chan, S. Pasteris, "Joint coreset construction and quantization for distributed machine learning," in IFIP Networking, Jun. 2020 (acceptance rate: 27.5%). [Proceedings Link]
15. C. Liu, X. He, T. Chanyaswad, S. Wang, P. Mittal, "Investigating statistical privacy frameworks from the perspective of hypothesis testing," in Privacy Enhancing Technologies Symposium (PETS), Jul. 2019 (acceptance rate: 22.3%). [DOI]
16. T. Tuor, S. Wang, K. K. Leung, B. J. Ko, "Online collection and forecasting of resource utilization in large-scale distributed systems," in IEEE International Conference on Distributed Computing Systems (ICDCS), Jul. 2019 (acceptance rate: 19.6%). [DOI]
17. S. Pasteris, S. Wang, M. Herbster, T. He, "Service placement with provable guarantees in heterogeneous edge computing systems," in IEEE International Conference on Computer Communications (INFOCOM), Apr. 2019 (acceptance rate: 19.7%). [DOI]
18. Y. Lin, T. He, S. Wang, K. Chan, S. Pasteris, "Looking glass of NFV: inferring the structure and state of NFV network from external observations," in IEEE International Conference on Computer Communications (INFOCOM), Apr. 2019 (acceptance rate: 19.7%). [DOI] [Journal Version]
19. V. Farhadi, F. Mehmeti, T. He, T. La Porta, H. Khamfroush, S. Wang, K. Chan, "Service placement and request scheduling for data-intensive applications in edge clouds," in IEEE International Conference on Computer Communications (INFOCOM), Apr. 2019 (acceptance rate: 19.7%). [DOI] [Journal Version]
20. T. He, H. Khamfroush, S. Wang, T. La Porta, S. Stein, "It's hard to share: joint service placement and request scheduling in edge clouds with sharable and non-sharable resources," in IEEE International Conference on Distributed Computing Systems (ICDCS), July 2018 (acceptance rate: 20%). [DOI]
21. S. Wang, T. Tuor, T. Salonidis, K. K. Leung, C. Makaya, T. He, K. Chan, "When edge meets learning: adaptive control for resource-constrained distributed machine learning," in IEEE International Conference on Computer Communications (INFOCOM), Apr. 2018 (acceptance rate: 19.2%). [DOI] [Journal Version] [Code of Journal Version]
22. I.-H. Hou, T. Zhao, S. Wang, K. Chan, "Asymptotically optimal algorithm for online reconfiguration of edge-clouds," in Proc. of ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (MobiHoc) 2016 (acceptance rate: 18.7%). [DOI] [Journal Version] [Code]
23. R. Urgaonkar, S. Wang, T. He, M. Zafer, K. Chan, K. K. Leung, "Dynamic service migration and workload scheduling in edge-clouds," in Proc. of IFIP International Symposium on Computer Performance, Modeling, Measurements and Evaluation (Performance) 2015, Oct. 2015 (acceptance rate: 28.40%). [DOI]
24. S. Wang, R. Urgaonkar, M. Zafer, T. He, K. Chan, K. K. Leung, "Dynamic service migration in mobile edge-clouds," in Proc. of IFIP Networking 2015, May 2015 (acceptance rate: 23.27%). [DOI] [Journal Version] [Code of Journal Version]

Journals & Magazines

1. J. Perazzone, S. Wang, M. Ji, K. Chan, "Communication-efficient device scheduling for federated learning using Lyapunov optimization," IEEE/ACM Transactions on Networking, accepted for publication, Feb. 2025. [DOI]
2. C. Yu, S. Shen, S. Wang, K. Zhang, H. Zhao, "Communication-efficient hybrid federated learning for E-health with horizontal and vertical data partitioning," IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 3, Mar. 2025. [DOI]
3. T. Castiglia, S. Wang, S. Patterson, "Flexible vertical federated learning with heterogeneous parties," IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 12, Dec. 2024. [DOI]
4. B. Luo, W. Xiao, S. Wang, J. Huang, L. Tassiulas, "Adaptive heterogeneous client sampling for federated learning over wireless networks," IEEE Transactions on Mobile Computing, vol. 23, no. 10, Oct. 2024. [DOI]
5. X. Liu, S. Wang, Y. Deng, A. Nallanathan, "Adaptive federated pruning in hierarchical wireless networks," IEEE Transactions on Wireless Communications, vol. 23, no. 6, pp. 5985 – 5999, Jun. 2024. [DOI]
6. Y. Jiang, S. Wang, V. Valls, B. J. Ko, W.-H. Lee, K. K. Leung, L. Tassiulas, "Model pruning enables efficient federated learning on edge devices," IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 12, pp. 10374 – 10386, Dec. 2023. [DOI] [Code]
7. S. Shen, Y. Han, X. Wang, S. Wang, V. C. M. Leung, "Collaborative learning-based scheduling for Kubernetes-oriented edge-cloud network," IEEE/ACM Transactions on Networking, vol. 31, no. 6, pp. 2950 – 2964, Dec. 2023. [DOI]
8. C.-C. Chiu, X. Zhang, T. He, S. Wang, A. Swami, "Laplacian matrix sampling for communication-efficient decentralized learning," IEEE Journal on Selected Areas in Communications, vol. 41, no. 4, pp. 887 – 901, Apr. 2023. [DOI]
9. Z. Chen, K. K. Leung, S. Wang, L. Tassiulas, K. Chan, D. Towsley, "Use coupled LSTM networks to solve constrained optimization problems," IEEE Transactions on Cognitive Communications and Networking, vol.9, no. 2, pp. 304 – 316, Apr. 2023. [DOI]
10. A. Das, T. Castiglia, S. Wang, S. Patterson, "Cross-silo federated learning for multi-tier networks with vertical and horizontal data partitioning," ACM Transactions on Intelligent Systems and Technology, vol. 13, no. 6, Dec. 2022. [DOI]
11. H. Lu, T. He, S. Wang, C. Liu, M. Mahdavi, V. Narayanan, K. Chan, S. Pasteris, "Communication-efficient k-means for edge-based machine learning," IEEE Transactions on Parallel and Distributed Systems, vol. 33, no. 10, pp. 2509 – 2523, Oct. 2022. [DOI]
12. A. Imteaj, U. Thakker, S. Wang, J. Li, M. H. Amini, "A survey on federated learning for resource-constrained IoT devices," IEEE Internet of Things Journal, vol. 9, no. 1, pp. 1 – 24, Jan. 2022. [DOI]
13. B. Luo, X. Li, S. Wang, J. Huang, L. Tassiulas, "Cost-Effective federated learning in mobile edge networks," IEEE Journal on Selected Areas in Communications, vol. 39, no. 12, pp. 3606 – 3621, Dec. 2021. [DOI]
14. V. Farhadi, F. Mehmeti, T. He, T. La Porta, H. Khamfroush, S. Wang, K. Chan, K. Poularakis, "Service placement and request scheduling for data-intensive applications in edge clouds," IEEE/ACM Transactions on Networking, vol. 29, no. 2, pp. 779 – 792, Apr. 2021. [DOI]
15. S. Wang, "Efficient deep learning," Nature Computational Science, vol. 1, pp. 181 – 182, Mar. 2021, invited News & Views paper. [DOI]
16. H. Lu, M.-J. Li, T. He, S. Wang, V. Narayanan, K. Chan, "Robust Coreset Construction for Distributed Machine Learning," IEEE Journal on Selected Areas in Communications, vol. 38, no. 10, pp. 2400 – 2417, Oct. 2020 (acceptance rate of this special issue of the journal: 20.8%). [DOI] [Code]
17. Y. Lin, T. He, S. Wang, K. Chan, S. Pasteris, "Looking glass of NFV: inferring the structure and state of NFV network from external observations," IEEE/ACM Transactions on Networking, vol. 28, no. 4, pp. 1477 – 1490, Aug. 2020. [DOI] [Code]
18. S. Wang, T. Tuor, T. Salonidis, K. K. Leung, C. Makaya, T. He, K. Chan, "Adaptive federated learning in resource constrained edge computing systems," IEEE Journal on Selected Areas in Communications, vol. 37, no. 6, pp. 1205 – 1221, Jun. 2019 (acceptance rate of this special issue of the journal: 13%, received the IEEE Communications Society Leonard G. Abraham Prize in 2021). [DOI] [Code]
19. S. Wang, R. Urgaonkar, M. Zafer, T. He, K. Chan, K. K. Leung, "Dynamic service migration in mobile edge computing based on Markov decision process," IEEE/ACM Transactions on Networking, vol. 27, no. 3, pp. 1272 – 1288, Jun. 2019. [DOI] [Code]
20. T. Zhao, I.-H. Hou, S. Wang, K. Chan, "ReD/LeD: an asymptotically optimal and scalable online algorithm for service caching at the edge," IEEE Journal on Selected Areas in Communications, vol. 36, no. 8, pp. 1857 – 1870, Aug. 2018. [DOI] [Code]
21. A. Machen, S. Wang, K. K. Leung, B. J. Ko, T. Salonidis, "Live service migration in mobile edge clouds," IEEE Wireless Communications, vol. 25, no. 1, pp. 140 – 147, Feb. 2018. [DOI]
22. T. He, E. N. Ciftcioglu, S. Wang, K. Chan, "Location privacy in mobile edge clouds: a chaff-based approach," IEEE Journal on Selected Areas in Communications, vol. 35, no. 11, pp. 2625 – 2636, Nov. 2017. [DOI]
23. S. Wang, R. Urgaonkar, T. He, K. Chan, M. Zafer, K. K. Leung, "Dynamic service placement for mobile micro-clouds with predicted future costs," IEEE Transactions on Parallel and Distributed Systems, vol. 28, no. 4, pp. 1002 – 1016, Apr. 2017. [DOI]
24. S. Wang, M. Zafer, K. K. Leung, "Online placement of multi-component applications in edge computing environments," IEEE Access, vol. 5, pp. 2514 – 2533, Feb. 2017. [DOI]
25. F. Wang, L. Guo, S. Wang, Q. Song, A. Jamalipour, "Approaching single-hop performance in multi-hop networks: end-to-end known-interference cancellation (E2E-KIC)," IEEE Transactions on Vehicular Technology, vol. 65, no. 9, pp. 7606 – 7620, Sept. 2016. [DOI]
26. R. Urgaonkar, S. Wang, T. He, M. Zafer, K. Chan, K. K. Leung, "Dynamic service migration and workload scheduling in edge-clouds," Performance Evaluation, vol. 91, pp. 205 – 228, Sept. 2015 (accepted directly through IFIP Performance 2015). [DOI]
27. Q. Song, L. Guo, F. Wang, S. Wang, A. Jamalipour, "MAC-centric cross-layer collaboration: a case study on physical-layer network coding," IEEE Wireless Communications, vol. 21, no. 6, pp. 160 – 166, Dec. 2014. [DOI]
28. Y. Huang, S. Wang, Q. Song, L. Guo, A. Jamalipour, "Synchronous physical-layer network coding: a feasibility study," IEEE Transactions on Wireless Communications, vol. 12, no. 8, pp. 4048 – 4057, Aug. 2013. [DOI]
29. S. Wang, Q. Song, X. Wang, A. Jamalipour, "Distributed MAC protocol supporting physical-layer network coding," IEEE Transactions on Mobile Computing, vol. 12, no. 5, pp. 1023 – 1036, May 2013. [DOI]
30. F. Wang, S. Wang, Q. Song, L. Guo, "Adaptive relaying method selection for multi-rate wireless networks with network coding," IEEE Communications Letters, vol. 16, no. 12, pp. 2004 – 2007, Dec. 2012. [DOI]
31. Q. Song, Z. Ning, S. Wang, A. Jamalipour, "Link stability estimation based on link connectivity changes in mobile ad-hoc networks," Journal of Network and Computer Applications, vol. 35, no. 6, pp. 2051 – 2058, Nov. 2012. [DOI]
32. S. Wang, Q. Song, X. Wang, A. Jamalipour, "Rate and power adaptation for analog network coding," IEEE Transactions on Vehicular Technology, vol. 60, no. 5, pp. 2302 – 2313, June 2011. [DOI]

Book Chapters

1. S. Wang, T. Tuor, K. K. Leung, "Optimized federated learning in wireless networks with constrained resources," in Machine Learning and Wireless Communications, Y. Eldar, A. Goldsmith, D. Gündüz and H. V. Poor (Eds.), Cambridge University Press, 2022.
2. G. Joshi, S. Wang, "Communication-efficient distributed optimization algorithms," in Federated Learning, H. Ludwig and N. Baracaldo (Eds.), Springer Nature, 2022.

Other Conferences, Workshops & Short Papers (all are peer-reviewed)

1. R. Zhang, H. Woisetschläger, S. Wang, H.-A. Jacobsen, "MESS+: energy-optimal inferencing in language model zoos with service level guarantees," in Workshop on Adaptive Foundation Models in Conjunction with NeurIPS 2024, Dec. 2024.
2. H. Woisetschläger, A. Erben, B. Marino, S. Wang, N. D. Lane, R. Mayer, H.-A. Jacobsen, "Federated learning priorities under the European Union Artificial Intelligence Act," in Workshop on Generative AI and Law (GenLaw) in Conjunction with ICML 2024, Jul. 2024.
3. D. Jhunjhunwala, N. Jali, G. Joshi, S. Wang, "Erasure coded neural network inference via Fisher averaging," in International Symposium on Information Theory (ISIT), Jul. 2024. [DOI]
4. H. Woisetschläger, A. Erben, S. Wang, R. Mayer, H.-A. Jacobsen, "Federated fine-tuning of LLMs on the very edge: the good, the bad, the ugly," in Workshop on Data Management for End-to-End Machine Learning (DEEM) in Conjunction with SIGMOD 2024, Jun. 2024. [DOI]
5. H. Wang, S. Wang, Q. Ji, "Semantic attribution for explainable uncertainty quantification," in Workshop on Epistemic Uncertainty in Artificial Intelligence, in Conjunction with UAI 2023 (E-pi UAI), Aug. 2023.
6. T. Castiglia, S. Wang, S. Patterson, "Self-supervised vertical federated learning," in Workshop on Federated Learning: Recent Advances and New Challenges, in Conjunction with NeurIPS 2022 (FL-NeurIPS’22), Dec. 2022.
7. C. Yu, S. Shen, S. Wang, K. Zhang, H. Zhao "Efficient multi-Layer stochastic gradient descent algorithm for federated learning in E-health," in IEEE International Conference on Communications (ICC), May 2022. [DOI]
8. Z. Chen, K. K. Leung, S. Wang, L. Tassiulas, K. Chan, "Robust solutions to constrained optimization problems by LSTM networks," in IEEE MILCOM 2021, Nov.-Dec. 2021. [DOI]
9. P. Han, J. Park, S. Wang, Y. Liu, "Robustness and diversity seeking data-free knowledge distillation," in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Jun. 2021. [DOI] [Code]
10. T. Tuor, S. Wang, B. J. Ko, C. Liu, K. K. Leung, "Overcoming noisy and irrelevant data in federated learning," in the 25th International Conference on Pattern Recognition (ICPR), Jan. 2021. [DOI] [Code]
11. Y. Jiang, S. Wang, V. Valls, B. J. Ko, W.-H. Lee, K. K. Leung, L. Tassiulas, "Model pruning enables efficient federated learning on edge devices," in Workshop on Scalability, Privacy, and Security in Federated Learning (SpicyFL) in Conjunction with NeurIPS 2020, long talk (acceptance rate: approximately 12.5%), Dec. 2020. [Journal Version] [Code]
12. T. Inoue, P. Vinayavekhin, S. Morikuni, S. Wang, T. H. Trong, D. Wood, M. Tatsubori, R. Tachibana, "Detection of anomalous sounds for machine condition monitoring using classification confidence," in Detection and Classification of Acoustic Scenes and Events (DCASE) Workshop 2020, Nov. 2020.
13. H. Lu, C. Liu, T. He, S. Wang, K. Chan, "Sharing models or coresets: a study based on membership inference attack," in International Workshop on Federated Learning for User Privacy and Data Confidentiality in Conjunction with ICML 2020 (FL-ICML'20), long talk, Jul. 2020.
14. Y. Lin, T. He, S. Wang, K. Chan, "Waypoint-based topology inference," in IEEE International Conference on Communications (ICC), Jun. 2020. [DOI]
15. P. Han, S. Wang, K. K. Leung, "Capacity analysis of distributed computing systems with multiple resource types," in IEEE Wireless Communications and Networking Conference (WCNC), May 2020. [DOI]
16. A. Feraudo, P. Yadav, V. Safronov, D. A. Popescu, R. Mortier, S. Wang, P. Bellavista, J. Crowcroft, "CoLearn: enabling federated learning in MUD compliant IoT edge networks," in International Workshop on Edge Systems, Analytics and Networking (EdgeSys), in conjunction with ACM EuroSys, Apr. 2020. [DOI]
17. H. Lu, M.-J. Li, T. He, S. Wang, V. Narayanan, K. Chan, "Robust coreset construction for distributed machine learning," in IEEE Global Communications Conference (GLOBECOM), Dec. 2019. [DOI] [Journal Version]
18. J.-W. Ahn, K. Grueneberg, B. J. Ko, W.-H. Lee, E. Morales, S. Wang, X. Wang, D. Wood, "Acoustic anomaly detection system: demo abstract," in ACM Conference on Embedded Networked Sensor Systems (SenSys), Nov. 2019. [DOI]
19. T. Inoue, P. Vinayavekhin, S. Wang, D. Wood, A. Munawar, B. Ko, N. Greco, R. Tachibana, "Shuffling and Mixing Data Augmentation for Environmental Sound Classification," in Detection and Classification of Acoustic Scenes and Events (DCASE) Workshop, Oct. 2019. [DOI]
20. A. Baughman, E. Morales, G. Reiss, N. Greco, S. Hammer, S. Wang, "Detection of tennis events from acoustic data," in ACM Workshop on Multimedia Content Analysis in Sports (MMSports), in conjunction with ACM International Conference on Multimedia (ACM Multimedia), Oct. 2019. [DOI]
21. W.-H. Lee, B. J. Ko, S. Wang, C. Liu, K. K. Leung, "Exact incremental and decremental learning for LS-SVM," in the 26th IEEE International Conference on Image Processing (ICIP), Sept. 2019 (best paper finalist, top 20 out of 2,065 submitted papers). [DOI]
22. B. J. Ko, S. Wang, T. He, D. Conway-Jones, "On data summarization for machine Learning in multi-organization federations," in Workshop on Distributed Analytics InfraStructure and Algorithms for Multi-Organization Federations (DAIS), Jun. 2019. [DOI]
23. D. Conway-Jones, T. Tuor, S. Wang, K. K. Leung, "Demonstration of federated learning in a resource-constrained networked environment," in IEEE International Conference on Smart Computing (SMARTCOMP), Jun. 2019. [DOI]
24. S. Vhaduri, T. Van Kessel, B. J. Ko, D. Wood, S. Wang, T. Brunschwiler, "Nocturnal cough and snore detection in noisy environments using smartphone-microphones," in IEEE International Conference on Healthcare Informatics (ICHI), Jun. 2019. [DOI]
25. Y. Lin, T. He, S. Wang, K. Chan, S. Pasteris, "Multicast-based weight inference in general network topologies," in IEEE International Conference on Communications (ICC), May 2019. [DOI]
26. T. Tuor, S. Wang, K. K. Leung, K. Chan, "Distributed machine learning in coalition environments: overview of techniques," in the 21st International Conference on Information Fusion (FUSION), July 2018. [DOI]
27. T. Tuor, S. Wang, T. Salonidis, B. J. Ko, K. K. Leung, "Demo abstract: distributed machine learning at resource-limited edge nodes," in IEEE International Conference on Computer Communications (INFOCOM), Apr. 2018. [DOI]
28. T. Tuor, S. Wang, K. K. Leung, B. J. Ko, "Understanding information leakage of distributed inference with deep neural networks: Overview of information theoretic approach and initial results," in Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR IX, Apr. 2018. [DOI]
29. D. Wood, S. Wang, T. Salonidis, D. Conway-Jones, B. J. Ko, G. White, "Distributed analytics for audio sensing applications," in Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR IX, Apr. 2018. [DOI]
30. S. Pasteris, S. Wang, C. Makaya, K. Chan, M. Herbster, "Data distribution and scheduling for distributed analytics tasks," in Workshop on Distributed Analytics InfraStructure and Algorithms for Multi-Organization Federations (DAIS), Aug. 2017. [DOI]
31. T. He, E. N. Ciftcioglu, S. Wang, K. Chan, "Location privacy in mobile edge clouds," in Proc. of the 37th IEEE International Conference on Distributed Computing Systems (ICDCS), short paper, Jun. 2017. [DOI] [Journal Version]
32. D. Verma, B. J. Ko, S. Wang, X. Wang, G. Bent, "Audio analysis as a control knob for social sensing," in Proc. of the 2nd International Workshop on Social Sensing (SocialSens'17), Apr. 2017. [DOI]
33. S. Wang, J. Ortiz, "Non-negative matrix factorization of signals with overlapping events for event detection applications," in Proc. of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Mar. 2017. [DOI]
34. B. J. Ko, J. Ortiz, T. Salonidis, M. Touma, D. Verma, S. Wang, X. Wang, D. Wood, "Demo abstract: acoustic signal processing for anomaly detection in machine room environments," in Proc. of ACM International Conference on Systems for Energy-Efficient Built Environments (BuildSys) 2016. [DOI]
35. A. Machen, S. Wang, K. K. Leung, B. J. Ko, T. Salonidis, "Poster: migrating running applications across mobile edge clouds," in Proc. of ACM International Conference on Mobile Computing and Networking (MobiCom) 2016. [DOI] [Journal Version]
36. S. Wang, K. Chan, R. Urgaonkar, T. He, K. K. Leung, "Emulation-based study of dynamic service placement in mobile micro-clouds," in Proc. of IEEE MILCOM 2015, Oct. 2015. [DOI]
37. Y. Yang, S. Wang, Q. Song, L. Guo, A. Jamalipour, "Double auction and negotiation for dynamic resource allocation with elastic demands," in Proc. of IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) 2015, Aug. – Sept. 2015. [DOI]
38. S. Wang, R. Urgaonkar, K. Chan, T. He, M. Zafer, K. K. Leung, "Dynamic service placement for mobile micro-clouds with predicted future costs," in Proc. of IEEE International Conference on Communications (ICC) 2015, Jun. 2015. [DOI] [Journal Version]
39. F. Wang, L. Guo, S. Wang, Y. Yu, Q. Song, A. Jamalipour, "Almost as good as single-hop full-duplex: bidirectional end-To-end known interference cancellation," in Proc. of IEEE International Conference on Communications (ICC) 2015, Jun. 2015. [DOI]
40. S. Wang, R. Urgaonkar, T. He, M. Zafer, K. Chan, K. K. Leung, "Mobility-induced service migration in mobile micro-clouds," in Proc. of IEEE MILCOM 2014, Oct. 2014. [DOI]
41. L. Zhang, Y. Yu, F. Huang, Q. Song, L. Guo, S. Wang, "Deadline-aware adaptive packet scheduling and transmission in cooperative wireless networks," in Proc. of IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) 2014, Sept. 2014. [DOI]
42. F. Wang, Q. Song, S. Wang, L. Guo, "Rate and power adaptation for physical-layer network coding with M-QAM modulation," in Proc. of IEEE International Conference on Communications (ICC) 2014, Jun. 2014. [DOI]
43. S. Wang, L. Le, N. Zahariev, K. K. Leung, "Centralized rate control mechanism for cellular-based vehicular networks," in Proc. of IEEE Global Communications Conference (GLOBECOM) 2013, Dec. 2013. [DOI]
44. F. Huang, S. Wang, Q. Song, L. Guo, A. Jamalipour, "Joint encoding and node-pair grouping for physical-layer network coding," in Proc. of IEEE Global Communications Conference (GLOBECOM) 2013, Dec. 2013. [DOI]
45. F. Wang, Q. Song, S. Wang, L. Guo, A. Jamalipour, "MAC protocol supporting physical-layer network coding with overhearing," in Proc. of IEEE Global Communications Conference (GLOBECOM) 2013, Dec. 2013. [DOI]
46. S. Wang, G.-H. Tu, R. Ganti, T. He, K. K. Leung, H. Tripp, K. Warr, Murtaza Zafer, "Mobile micro-cloud: application classification, mapping, and deployment," in Annual Fall Meeting of the ITA, Oct. 2013.
47. S. Wang, Q. Song, L. Guo, A. Jamalipour, "Constellation mapping for physical-layer network coding with M-QAM modulation," in Proc. of IEEE Global Communications Conference (GLOBECOM) 2012, Dec. 2012. [DOI]
48. Y. Huang, Q. Song, S. Wang, A. Jamalipour, "Phase-level synchronization for physical-layer network coding," in Proc. of IEEE Global Communications Conference (GLOBECOM) 2012, Dec. 2012. [DOI]
49. Y. Huang, Q. Song, S. Wang, A. Jamalipour, "Symbol error rate analysis for M-QAM modulated physical-layer network coding with phase errors," in Proc. of IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) 2012, Sept. 2012. [DOI]
50. S. Wang, Q. Song, J. Feng, X. Wang, "Predicting the link stability based on link connectivity changes in mobile ad hoc networks," in Proc. of IEEE International Conference on Wireless Communications, Networking and Information Security (WCNIS) 2010, vol. 2, pp. 409 – 414, 2010. [DOI]
51. S. Wang, M. Wang, H. Hong, Y. Ma, "Environmental monitoring system based on sensor networks using multi-channel MAC protocol," in Proc. of International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM) 2009, pp. 1 – 4, 2009. [DOI]
52. S. Wang, M. Wang, Y. Ma, H. Hong, "Series connected buck-boost type solar power converter based on microcontroller," in Proc. of IEEE International Conference on Mechatronics and Automation (ICMA) 2009, pp. 2642 – 2646, 2009. [DOI]
53. S. Wang, Y. He, Z. Liu, H. Wu, "Personalized web based English learning system using artificial neural networks," in Proc. of International Conference on Computer Science & Education (ICCSE) 2009, pp. 1263 – 1268, 2009. [DOI]

Patents

Patent applications and granted patents that have been published in public are listed below. Recent applications that have not been published remain confidential and are not included in this list. Note: Some items list inventors in alphabetical order (by last name or first name).

1. N. Greco, S. Wang, N. Desai. Automated attribute-based machine learning model selection, US20250181893A1, Dec. 2023, filed.
2. T. Castiglia, Y. Zhou, N. Baracaldo, S. Kadhe, S. Wang, S. Patterson. Feature selection in vertical federated learning, US20240242087A1, Jan. 2023, filed.
3. A. Mukherjee, G. Kollias, T. Salonidis, S. Wang. Sketched and clustered federated learning with automatic tuning, US20240070531A1, Jan. 2023, filed.
4. C. Liu, W.-H. Lee, S. Wang, S. Calo, D. Verma. Drone-assisted communications network, US20240107330A1, Sept. 2022, filed.
5. S. Wang, T. Castiglia, N. Baracaldo, S. Patterson, R. Xu, Y. Zhou. Vertical federated learning with secure aggregation, US20230401439A1, Jun. 2022, filed.
6. T. Castiglia, S. Wang, S. Patterson. Semi-supervised vertical federated learning, US20230342655A1, Apr. 2022, filed.
7. D. Verma, S. Chakraborty, S. Wang, A. Vega, H. Yueksel, A. Verma, P. Bose, J. K. Radhakrishnan. Data subset selection for federated learning, US20230281518A1, Mar. 2022, filed.
8. A. Das, T. Castiglia, S. Patterson, S. Wang. Vertical federated learning with compressed embeddings, US12033074B2, May 2021, granted.
9. H. Yueksel, B. Kingsbury, K. Varshney, P. Bose, D. Verma, S. Wang, A. Vega, A. Verma, S. Chakraborty. Input-encoding with federated learning, US20220343218A1, Apr. 2021, filed.
10. S. Wang, S. Chakraborty, N. Desai, D. Freimuth, W.-H. Lee, C. Liu. Federated ensemble learning from decentralized data with incremental and decremental updates, US20220121999A1, Oct. 2020, filed.
11. W.-H. Lee, C. Liu, S. Wang, B. J. Ko, Y. Jiang. Incremental and decentralized model pruning in federated machine learning, US11842260B2, Sept. 2020, granted.
12. S. Wang, G. Kollias, T. Salonidis. Federated machine learning using locality sensitive hashing, US11620583B2, Sept. 2020, granted.
13. G. Kollias, T. Salonidis, S. Wang. Tensor comparison across a distributed machine learning environment, US11954611B2, Aug. 2020, granted.
14. S. Wang, T. Tuor, C. Liu, F. Le. Adaptive asynchronous federated learning, US11574254B2, Apr. 2020, granted.
15. D. Verma, R. Raghavendra, B. J. Ko, M. Srivatsa, N. Desai, R. Ganti, S. Wang, S. Chakraborty. Base station beam management based on terminal transmit data indication, US11296771B2, Mar. 2020, granted.
16. D. Verma, R. Raghavendra, B. J. Ko, M. Srivatsa, N. Desai, R. Ganti, S. Wang, S. Chakraborty. Cost effective delivery of network connectivity to remote areas, US11228961B2, Mar. 2020, granted.
17. T. Tuor, S. Wang, C. Liu, B. J. Ko, W.-H. Lee. Federated learning of clients, US11461593B2, Nov. 2019, granted.
18. P. Novotny, S. Wang, Q. Zhang, V. Ramakrishna. Optimization of delivery of blocks, US11689616B2, May 2019, granted.
19. C. Liu, S. Wang, W.-H. Lee, S. Calo. Leveraging correlation across agents for enhanced distributed machine learning, US11521091B2, May 2019, granted.
20. M. Srivatsa, S. Wang, J. Rosenkranz, S. Chakraborty, B. J. Ko. Determining value of corpora for machine learning using coresets, US11526800B2, May 2019, granted.
21. S. Wang, I. Manotas, B. J. Ko, K. Grueneberg. User adapted data presentation for data labeling, US11132623B2, Oct. 2018, granted.
22. S. Wang, T. Salonidis. Collaborative distributed machine learning, US11521090B2, Aug. 2018, granted.
23. S. Wang, T. Tuor, T. Salonidis, C. Makaya, B. J. Ko. Distributed machine learning at edge nodes, US11836576B2, Apr. 2018, granted.
24. M. Beigi, S. Calo, D. Verma, S. Wang, D. Wood. Flexible and self-adaptive classification of received audio measurements in a network environment, US10121109B2, Apr. 2017, granted.
25. L. Le, N. Zahariev, S. Wang. Adaptive rate control for cellular-based vehicular networks, US10044589B2, Jan. 2013, granted.

Thesis

1. S. Wang, Dynamic service placement in mobile micro-clouds, Ph.D. Thesis, Imperial College London, 2015.

Technical Reports

1. T. Inoue, P. Vinayavekhin, S. Morikuni, S. Wang, T. H. Trong, D. Wood, M. Tatsubori, R. Tachibana, "Detection of anomalous sounds for machine condition monitoring using classification confidence," in Detection and Classification of Acoustic Scenes and Events (DCASE) Challenge 2020, Jun. 2020 (ranked 4th out of 40 teams in DCASE Challenge 2020, Task 2).
2. A. Imteaj, U. Thakker, S. Wang, J. Li, M. H. Amini, "Federated learning for resource-constrained IoT devices: Panoramas and state-of-the-art," Feb. 2020.
3. J. Park, S. Wang, A. Elgabli, S. Oh, E. Jeong, H. Cha, H. Kim, S.-L. Kim, M. Bennis, "Distilling on-device intelligence at the network edge," Aug. 2019.
4. T. Inoue, P. Vinayavekhin, S. Wang, D. Wood, N. Greco, R. Tachibana, "Domestic activities classification based on CNN using shuffling and mixing data augmentation," in Detection and Classification of Acoustic Scenes and Events (DCASE) Challenge 2018, Sept. 2018 (ranked first in DCASE Challenge 2018, Task 5).
5. S. Wang, Q. Song, K. Wu, F. Wang, L. Guo, "End-to-End Known-Interference Cancellation (E2E-KIC) with Multi-Hop Interference," Mar. 2016.
6. F. Wang, L. Guo, S. Wang, Q. Song, A. Jamalipour, "Approaching single-hop performance in multi-hop networks: end-to-end known-interference cancellation (E2E-KIC) – long version," Sept. 2015.
7. S. Wang, R. Urgaonkar, M. Zafer, T. He, K. Chan, K. K. Leung, "Supplementary materials for dynamic service migration in mobile edge-clouds," Mar. 2015.