CFLMEC: Cooperative Federated Learning for Mobile Edge Computing

TL;DR

This paper proposes CFLMEC, a cooperative federated learning framework for mobile edge computing that optimizes communication resources and enhances throughput amid spectrum scarcity and interference.

Contribution

It introduces a novel resource allocation approach for federated learning in mobile edge computing, considering interference and spectrum constraints, with algorithms for different reliance levels on edge servers.

Findings

CFLMEC achieves higher device throughput than existing methods.

The proposed algorithms effectively manage spectrum and interference.

Simulation results validate the framework's efficiency in real-world scenarios.

Abstract

We investigate a cooperative federated learning framework among devices for mobile edge computing, named CFLMEC, where devices co-exist in a shared spectrum with interference. Keeping in view the time-average network throughput of cooperative federated learning framework and spectrum scarcity, we focus on maximize the admission data to the edge server or the near devices, which fills the gap of communication resource allocation for devices with federated learning. In CFLMEC, devices can transmit local models to the corresponding devices or the edge server in a relay race manner, and we use a decomposition approach to solve the resource optimization problem by considering maximum data rate on sub-channel, channel reuse and wireless resource allocation in which establishes a primal-dual learning framework and batch gradient decent to learn the dynamic network with outdated information and predict the sub-channel condition. With aim at maximizing throughput of devices, we propose communication resource allocation algorithms with and without sufficient sub-channels for strong reliance on edge servers (SRs) in cellular link, and interference aware communication resource allocation algorithm for less reliance on edge servers (LRs) in D2D link. Extensive simulation results demonstrate the CFLMEC can achieve the highest throughput of local devices comparing with existing works, meanwhile limiting the number of the sub-channels.