A Survey on Federated Learning for Resource-Constrained IoT Devices
- Ahmed Imteaj
- Urmish Thakker
- et al.
- 2021
- IEEE IoT Journal
Shiqiang Wang is a Staff Research Scientist at IBM T. J. Watson Research Center, NY, USA. He received his Ph.D. from Imperial College London, United Kingdom, in 2015. His current research focuses on the intersection of distributed computing, machine learning, networking, and optimization, with a broad range of applications including data analytics, edge-based artificial intelligence (Edge AI), Internet of Things (IoT), and future wireless systems. He has made foundational contributions to edge computing and federated learning that generated both academic and industrial impact. Dr. Wang serves as an associate editor of the IEEE Transactions on Mobile Computing and IEEE Transactions on Parallel and Distributed Systems. He has also been actively organizing workshops at the intersection of edge computing and machine learning, and regularly participates in technical program committees (TPCs) of prominent conferences and review panels of research grants. He received the IEEE Communications Society (ComSoc) Leonard G. Abraham Prize in 2021, IEEE ComSoc Best Young Professional Award in Industry in 2021, IBM Outstanding Technical Achievement Awards (OTAA) in 2019, 2021, 2022, and 2023, multiple Invention Achievement Awards from IBM since 2016, Best Paper Finalist of the IEEE International Conference on Image Processing (ICIP) 2019, and Best Student Paper Award of the Network and Information Sciences International Technology Alliance (NIS-ITA) in 2015.
For details with latest news and full list of publications, please visit Dr. Shiqiang Wang's personal homepage at: https://shiqiang.wang/