33 citations · 45 across the 4 of their papers we have counts for
4 papers
Fair Concurrent Training of Multiple Models in Federated Learning
Marie Siew, Haoran Zhang, Jong-Ik Park +6
Federated learning (FL) enables collaborative learning across multiple clients. In most FL work, all clients train a single learning task. However, the recent proliferation of FL a…
FedSoft: Soft Clustered Federated Learning with Proximal Local Updating
Yichen Ruan, Carlee Joe-Wong
Traditionally, clustered federated learning groups clients with the same data distribution into a cluster, so that every client is uniquely associated with one data distribution an…
Towards Flexible Device Participation in Federated Learning
Yichen Ruan, Xiaoxi Zhang, Shu-Che Liang +1
Traditional federated learning algorithms impose strict requirements on the participation rates of devices, which limit the potential reach of federated learning. This paper extend…
Network-Aware Optimization of Distributed Learning for Fog Computing
Su Wang, Yichen Ruan, Yuwei Tu +3
Fog computing promises to enable machine learning tasks to scale to large amounts of data by distributing processing across connected devices. Two key challenges to achieving this…