FLCC: Efficient Distributed Federated Learning on IoMT over CSMA/CA

TL;DR

This paper proposes FLCC, an efficient federated learning approach over wireless IoMT networks using CSMA/CA, which improves throughput and model accuracy by leveraging spatial clustering and frequency reuse.

Contribution

The paper introduces a novel FL framework over CSMA/CA that employs spatial clustering and frequency allocation to enhance distributed learning in wireless healthcare networks.

Findings

Outperforms baseline FL algorithms in accuracy on MNIST dataset.

Achieves higher successful transmission probability with reduced interference.

Demonstrates robustness of FL in wireless IoMT environments.

Abstract

Federated Learning (FL) has emerged as a promising approach for privacy preservation, allowing sharing of the model parameters between users and the cloud server rather than the raw local data. FL approaches have been adopted as a cornerstone of distributed machine learning (ML) to solve several complex use cases. FL presents an interesting interplay between communication and ML performance when implemented over distributed wireless nodes. Both the dynamics of networking and learning play an important role. In this article, we investigate the performance of FL on an application that might be used to improve a remote healthcare system over ad hoc networks which employ CSMA/CA to schedule its transmissions. Our FL over CSMA/CA (FLCC) model is designed to eliminate untrusted devices and harness frequency reuse and spatial clustering techniques to improve the throughput required for coordinating a distributed implementation of FL in the wireless network. In our proposed model, frequency allocation is performed on the basis of spatial clustering performed using virtual cells. Each cell assigns a FL server and dedicated carrier frequencies to exchange the updated model's parameters within the cell. We present two metrics to evaluate the network performance: 1) probability of successful transmission while minimizing the interference, and 2) performance of distributed FL model in terms of accuracy and loss while considering the networking dynamics. We benchmark the proposed approach using a well-known MNIST dataset for performance evaluation. We demonstrate that the proposed approach outperforms the baseline FL algorithms in terms of explicitly defining the chosen users' criteria and achieving high accuracy in a robust network.