10 citations · 26 across the 5 of their papers we have counts for
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cs.LG2026
Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer
Longtao Xu, Jian Li
Real-world federated systems seldom operate on static data: input distributions drift while privacy rules forbid raw-data sharing. We study this setting as Federated Domain-Increme…
cs.LG2021★ 3 cited
Critical Learning Periods in Federated Learning
Gang Yan, Hao Wang, Jian Li
Federated learning (FL) is a popular technique to train machine learning (ML) models with decentralized data. Extensive works have studied the performance of the global model; howe…
cs.LG2021★ 10 cited
Straggler-Resilient Distributed Machine Learning with Dynamic Backup Workers
Guojun Xiong, Gang Yan, Rahul Singh +1
With the increasing demand for large-scale training of machine learning models, consensus-based distributed optimization methods have recently been advocated as alternatives to the…