Machine Learning at the Edge

• Theory, algorithms, and systems for efficient model training from local data at distributed edge devices/servers, including federated learning and other techniques (e.g., coreset).
• Highlights: communication interval adaptation [JSAC’19/INFOCOM’18], adaptive model pruning [SpicyFL@NeurIPS’20], gradient sparsification [ICDCS’20], relevant data selection [ICPR’20], robust coreset construction [JSAC’20]

Placement and Scheduling in Edge Computing

• Theory and algorithms for online decision making in edge computing and related systems/applications, for problems including service placement and workload scheduling.
• Highlights: dynamic service migration based on Markov decision process [ToN’19/Networking’15], 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]

Applications

• Machine learning algorithms and systems for various applications in edge and cloud environments, including detection and classification of environmental sounds. These developments have been an essential part in many practical use cases at IBM.

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, in press.
2. G. Joshi, S. Wang, "Communication-effcient distributed optimization algorithms", in Federated Learning, H. Ludwig and N. Baracaldo (Eds.), Springer Nature, in press.

Journal Papers (including magazines)

1. 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," submitted to IEEE Transactions on Neural Networks and Learning Systems, under second round of review, 2021. [Code]
2. 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," submitted to IEEE Transactions on Parallel and Distributed Systems, under revision after first round of review, 2021.
3. 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, accepted for publication, Oct. 2021. [DOI]
4. 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, accepted for publication, Jul. 2021. [DOI]
5. 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]
6. S. Wang, "Efficient deep learning," Nature Computational Science, vol. 1, pp. 181 – 182, Mar. 2021, invited News & Views paper. [DOI]
7. 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]
8. 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]
9. 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]
10. 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]
11. 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]
12. 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]
13. 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]
14. 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]
15. 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]
16. 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]
17. 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]
18. 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]
19. 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]
20. 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]
21. 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]
22. 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]
23. 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]

Conference Papers (peer-reviewed, including workshops)

1. 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]
2. 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]
3. 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]
4. 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] [Code]
5. 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.
6. 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]
7. 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, Dec. 2020. [Code]
8. 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%). [Code]
9. 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]
10. 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]
11. 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.
12. 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.
13. 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%).
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. 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]
23. 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]
24. 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]
25. 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]
26. 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]
27. 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]
28. 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]
29. 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]
30. 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]
31. 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.
32. 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]
33. 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]
34. 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]
35. 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]
36. 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]
37. 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]
38. 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]
39. 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]
40. 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]
41. 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]
42. 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]
43. 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]
44. 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]
45. 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]
46. 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]
47. 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]
48. 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]
49. 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]
50. 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]
51. 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]
52. 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]
53. 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]
54. 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]
55. 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]
56. 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]
57. 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.
58. 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]
59. 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]
60. 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]
61. 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.
62. 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.
63. 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.
64. 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.

Patents

20+ patent applications filed. Active 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. S. Wang, T. Tuor, C. Liu, F. Le. Adaptive asynchronous federated learning, US20210342749A1, Apr. 2020, filed.
2. 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, US20210281309A1, Mar. 2020, filed.
3. 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, US20210282067A1, Mar. 2020, filed.
4. T. Tuor, S. Wang, C. Liu, B. J. Ko, W.-H. Lee. Federated learning of clients, US20210158099A1, Nov. 2019, filed.
5. P. Novotny, S. Wang, Q. Zhang, V. Ramakrishna. Optimization of delivery of blocks, US20200374340A1, US20200374343A1, May 2019, filed.
6. C. Liu, S. Wang, W.-H. Lee, S. Calo. Leveraging correlation across agents for enhanced distributed machine learning, US20200372380A1, May 2019, filed.
7. M. Srivatsa, S. Wang, J. Rosenkranz, S. Chakraborty, B. J. Ko. Determining value of corpora for machine learning using coresets, US20200364613A1, May 2019, filed.
8. S. Wang, I. Manotas, B. J. Ko, K. Grueneberg. User adapted data presentation for data labeling, US11132623B2, Oct. 2018, granted.
9. S. Wang, T. Salonidis. Collaborative distributed machine learning, US20200050951A1, Aug. 2018, filed.
10. S. Wang, T. Tuor, T. Salonidis, C. Makaya, B. J. Ko. Distributed machine learning at edge nodes, US20190318268A1, Apr. 2018, filed.
11. S. Rallapalli, M. Srivatsa, S. Wang. Method and apparatus for combining independently evolved neural networks in a distributed environment, US20190130261A1, Oct. 2017, filed.
12. 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.
13. 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 number one 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.

Preprints

1. J. Wang, S. Wang, R.-R. Chen, M. Ji, "Local averaging helps: hierarchical federated learning and convergence analysis".