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20242026
most citedTowards Dynamic Resource Allocation and Client Scheduling in Hierarchical Federated Learning: A Two-Phase Deep Reinforcement Learning Approach

1 citations · 1 across the 7 of their papers we have counts for

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cs.LG2026

FedVSSAM: Mitigating Flatness Incompatibility in Sharpness-Aware Federated Learning

Bingnan Xiao, Yuan Gao, Bingcong Li +3

Sharpness-aware minimization (SAM) is an effective method for improving the generalization of federated learning (FL) by steering local training toward flat minima. Under data hete…

cs.LG2024★ 1 cited

Towards Dynamic Resource Allocation and Client Scheduling in Hierarchical Federated Learning: A Two-Phase Deep Reinforcement Learning Approach

Xiaojing Chen, Zhenyuan Li, Wei Ni +5

Federated learning (FL) is a viable technique to train a shared machine learning model without sharing data. Hierarchical FL (HFL) system has yet to be studied regrading its multip…