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cs.LG2024
A Robust Federated Learning Framework for Undependable Devices at Scale
Shilong Wang, Jianchun Liu, Hongli Xu +4
In a federated learning (FL) system, many devices, such as smartphones, are often undependable (e.g., frequently disconnected from WiFi) during training. Existing FL frameworks alw…
cs.LG2023
MergeSFL: Split Federated Learning with Feature Merging and Batch Size Regulation
Yunming Liao, Yang Xu, Hongli Xu +3
Recently, federated learning (FL) has emerged as a popular technique for edge AI to mine valuable knowledge in edge computing (EC) systems. To mitigate the computing/communication…
cs.LG2023
SemiSFL: Split Federated Learning on Unlabeled and Non-IID Data
Yang Xu, Yunming Liao, Hongli Xu +3
Federated Learning (FL) has emerged to allow multiple clients to collaboratively train machine learning models on their private data at the network edge. However, training and depl…