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cs.LG2024
On the Convergence Rates of Federated Q-Learning across Heterogeneous Environments
Leo Muxing Wang, Pengkun Yang, Lili Su
Large-scale multi-agent systems are often deployed across wide geographic areas, where agents interact with heterogeneous environments. There is an emerging interest in understandi…
cs.LG2024★ 2 cited
Towards Optimal Customized Architecture for Heterogeneous Federated Learning with Contrastive Cloud-Edge Model Decoupling
Xingyan Chen, Tian Du, Mu Wang +5
Federated learning, as a promising distributed learning paradigm, enables collaborative training of a global model across multiple network edge clients without the need for central…