Knowledge- and Model-Driven Deep Reinforcement Learning for Efficient Federated Edge Learning: Single- and Multi-Agent Frameworks

In this paper, we investigate federated learning (FL) efficiency improvement in practical edge computing systems, where edge workers have non-independent and identically distributed (non-IID) local data, as well as dynamic and heterogeneous computing and communication capabilities. We consider a gen...

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Vydané v:	IEEE transactions on machine learning in communications and networking Ročník 3; s. 332 - 352
Hlavní autori:	Li, Yangchen, Zhao, Lingzhi, Wang, Tianle, Ding, Lianghui, Yang, Feng
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	IEEE 2025
Predmet:	Accuracy algorithm parameter configuration Computational modeling Convergence deep reinforcement learning Delay effects Edge computing Energy consumption Federated learning Markov decision processes multi-agent reinforcement learning Quantization (signal) Servers Stochastic processes worker selection
ISSN:	2831-316X, 2831-316X
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