Accelerating convergence of federated learning in MEC with dynamic community

Wen Sun*, Yongjie Zhao, Wenqiang Ma, Bin Guo, Lexi Xu, Trung Q. Duong

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

Mobile edge computing (MEC) brings computational resources to the edge of network that triggers the paradigm shift of centralized machine learning towards federated learning. Federated learning enables edge nodes to collaboratively train a shared prediction model without sharing data. In MEC, heterogeneous edge nodes may join or leave the training phase during the federated learning process, resulting in slow convergence of dynamic communities and federated learning. In this paper, we propose a fine-grained training strategy for federated learning to accelerate its convergence rate in MEC with dynamic community. Based on multi-agent reinforcement learning, the proposed scheme enables each edge node to adaptively adjust its training strategy (aggregation timing and frequency) according to the network dynamics, while compromising with each other to improve the convergence of federated learning. To further adapt to the dynamic community in MEC, we propose a meta-learning-based scheme where new nodes can learn from other nodes and quickly perform scene migration to further accelerate the convergence of federated learning. Numerical results show that the proposed framework outperforms the benchmarks in terms of convergence speed, learning accuracy, and resource consumption.

Original languageEnglish
Pages (from-to)1769-1784
JournalIEEE Transactions on Mobile Computing
Volume23
Issue number2
Early online date02 Feb 2023
DOIs
Publication statusPublished - Feb 2024

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