2 citations · 2 across the 7 of their papers we have counts for
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cs.LG2024
FedCAda: Adaptive Client-Side Optimization for Accelerated and Stable Federated Learning
Liuzhi Zhou, Yu He, Kun Zhai +6
Federated learning (FL) has emerged as a prominent approach for collaborative training of machine learning models across distributed clients while preserving data privacy. However,…
cs.LG2024
FedEGG: Federated Learning with Explicit Global Guidance
Kun Zhai, Yifeng Gao, Difan Zou +4
Federated Learning (FL) holds great potential for diverse applications owing to its privacy-preserving nature. However, its convergence is often challenged by non-IID data distribu…
cs.LG2021★ 2 cited
Byzantine-Robust Federated Learning via Credibility Assessment on Non-IID Data
Kun Zhai, Qiang Ren, Junli Wang +1
Federated learning is a novel framework that enables resource-constrained edge devices to jointly learn a model, which solves the problem of data protection and data islands. Howev…