Christopher G. Brinton (Purdue University, USA); Seyyedali Hosseinalipour (University at Buffalo–SUNY, USA); Nicolo Michelusi (Arizona State University, USA); Osvaldo Simeone (King’s College London, UK)
Lay Importance on the Layer: Federated Learning for Non-IID data with Layer-based Regularization
Eleftherios Charteros and Iordanis Koutsopoulos (Athens University of Economics and Business, Greece)
The key idea is a new regularization term in the local loss function, which generalizes that of FedProx and captures divergence between global and local model weights of each client at the level of Deep Neural Network (DNN) layers. That is, the weights of different layers of the DNN are treated differently in the regularization function. Divergence between the global and local models is captured through a dissimilarity metric and a distance metric, both applied to each DNN layer. Since regularization is applied per layer and not universally to all weights as in FedProx, during local updates, only the weights of some layers that drift away from the global model are fine-tuned, while other weights are not affected. We verify the superior performance of FedLap over FedAvg and FedProx in terms of accuracy and convergence speed with different datasets, in settings with Non-IID data and unstable client participation. FedLap achieves 3-5% higher accuracy in the first 20 communication rounds compared to FedAvg and FedProx, while it achieves up to 10% higher accuracy in cases of unstable client participation.
Speaker Iordanis Koutsopoulos (Athens University of Economics and Business)
Iordanis Koutsopoulos is a Professor with the Department of Informatics of Athens University of Economics and Business, Athens, Greece.
Federated Learning at the Edge: An Interplay of Mini-batch Size and Aggregation Frequency
Weijie Liu and Xiaoxi Zhang (Sun Yat-sen University, China); Jingpu Duan (Peng Cheng Laboratory, China); Carlee Joe-Wong (Carnegie Mellon University, USA); Zhi Zhou and Xu Chen (Sun Yat-sen University, China)
Speaker Weijie Liu (Sun Yat-sen University )
Weijie Liu received the B.E. degree in electronics and communication engineering from the Sun Yat-sen University in 2021. He is currently working toward the M.E. degree at the School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China. His research interests include federated learning and edge computing.
Anarchic Convex Federated Learning
Dongsheng Li and Xiaowen Gong (Auburn University, USA)
Speaker Dongsheng Li (Auburn University)
He is currently a Ph.D. student in Electrical and Computer Engineering at Auburn University, USA. His main research interests include federated learning and wireless communication scheduling.
Efficient Communication-assisted Sensing based on Federated Transfer Learning
Wenjiang Ouyang, Mu Junsheng and Jingyan Wu (Beijing University of Posts and Telecommunications, China); Bohan Li (University of Southampton, United Kingdom (Great Britain)); Xiao jun Jing (Beijing University of Posts and Telecommunications, China)
Speaker Wenjiang Ouyang(Beijing University of Posts and Telecommunications)
Wenjiang Ouyang received master degrees in information and communication engineering from the Beijing University of Posts and Telecommunications in 2022. At present, he is currently pursuing his doctor degree at Beijing University of Posts and Telecommunications. His research interests include ISAC, wireless communication and artificial intelligence.
Christopher G. Brinton (Purdue University, USA)
Distributed Learning and Edge Computing
Uplink Power Control for Extremely Large-Scale MIMO with Multi-Agent Reinforcement Learning and Fuzzy Logic
Ziheng Liu, Zhilong Liu and Jiayi Zhang (Beijing Jiaotong University, China); Huahua Xiao (ZTE Corporation, China); Bo Ai (Beijing Jiaotong University & State Key Lab of Rail Traffic Control and Safety, China); Derrick Wing Kwan Ng (University of New South Wales, Australia)
Speaker Ziheng Liu (Beijing Jiaotong University, China)
Information Recycling Assisted Collaborative Edge Computing for Distributed Learning
Wanlin Liang (Wuhan University, China); Tianheng Li (WuHan University, China); Xiaofan He (Wuhan University, China)
Speaker Wanlin Liang (Wuhan University)
Coding-Aware Rate Splitting for Distributed Coded Edge Learning
Tianheng Li (WuHan University, China); Jingzhe Zhang and Xiaofan He (Wuhan University, China)
Speaker Tianheng Li (Wuhan University)
Pragmatic Communication: Bridging Neural Networks for Distributed Agents
Tianhao Guo (Shanxi Universitiy, China & Xidian University, China)
Speaker Tianhao Guo (Shanxi University&Xidian University)
Tianhao Guo (Member, IEEE) received the Ph.D. degree from the School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK in 2020. He is currently a lecturer in Shanxi University. He is also a postdoctoral research associate with the National Key Laboratory of Integrated Services Networks (ISN), Xidian University, China. His research interests include semantic and pragmatic communication, reconfigurable
intelligent surface-aided joint communication and sensing in coal mines, deep learning technologies for
wireless communications, etc. He is also serving as a review editor in Frontiers in Space Technologies and Frontiers in Computer Science. He has served as workshop co-chair of the ICSINC Big Data Workshop 2021. He has reviewed papers from IEEE Wireless Communications and Wireless Communications and Mobile Computing.
Seyyedali Hosseinalipour (University at Buffalo–SUNY, USA)