Clients Eligibility-Based Lightweight Protocol in Federated Learning: An IDS Use-Case

Federated learning (FL) enables clients to train models locally, enhancing privacy by avoiding data centralization. Traditional FL assumes all clients have adequate resources, an often unrealistic expectation in heterogeneous networks with resource constraints like limited battery, memory, and bandwidth. These limitations can hinder performance, prolong convergence times, and lead to inaccurate models. To address these challenges, we introduce the Client Eligibility-based Lightweight Protocol (CELP), optimized for resource-constrained environments. CELP employs a sample-based pruning mechanism and a re-parameterized FedAvg algorithm, enhancing its management of resource variability. It also integrates an intrusion detection system to safeguard against malicious activities. Our results show that CELP significantly reduces communication overhead by up to 81.01% compared to FedAvg and up to 72.54% compared to FedProx and enhances system stability, achieving 93% accuracy on the MNIST dataset and 83% accuracy on CIFAR-10. These improvements demonstrate CELP’s ability to deliver robust performance and efficiency in diverse FL scenarios.