Scalable and Low-Latency Federated Learning with Cooperative Mobile Edge Networking

• URL: http://arxiv.org/abs/2205.13054v1
• Date: Wed, 25 May 2022 21:18:12 GMT
• Title: Scalable and Low-Latency Federated Learning with Cooperative Mobile Edge Networking
• Authors: Zhenxiao Zhang, Zhidong Gao, Yuanxiong Guo, Yanmin Gong
• Abstract summary: Federated learning (FL) enables collaborative model training without centralizing data. This paper proposes a new FL framework based on cooperative mobile edge networking called cooperative federated edge learning (CFEL) to enable both high-accuracy and low-latency distributed intelligence at mobile edge networks.
• Score: 11.868792440783055
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated learning (FL) enables collaborative model training without centralizing data. However, the traditional FL framework is cloud-based and suffers from high communication latency. On the other hand, the edge-based FL framework that relies on an edge server co-located with access point for model aggregation has low communication latency but suffers from degraded model accuracy due to the limited coverage of edge server. In light of high-accuracy but high-latency cloud-based FL and low-latency but low-accuracy edge-based FL, this paper proposes a new FL framework based on cooperative mobile edge networking called cooperative federated edge learning (CFEL) to enable both high-accuracy and low-latency distributed intelligence at mobile edge networks. Considering the unique two-tier network architecture of CFEL, a novel federated optimization method dubbed cooperative edge-based federated averaging (CE-FedAvg) is further developed, wherein each edge server both coordinates collaborative model training among the devices within its own coverage and cooperates with other edge servers to learn a shared global model through decentralized consensus. Experimental results based on benchmark datasets show that CFEL can largely speed up the convergence speed and reduce the training time to achieve a target model accuracy compared with prior FL frameworks.

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