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

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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Bibliographic Details
Main Authors: Yangchen Li, Lingzhi Zhao, Tianle Wang, Lianghui Ding, Feng Yang
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Transactions on Machine Learning in Communications and Networking
Subjects:
Federated learning
edge computing
worker selection
algorithm parameter configuration
deep reinforcement learning
multi-agent reinforcement learning
Online Access:https://ieeexplore.ieee.org/document/10854500/
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