FedLSC: Improving Communication Efficiency and Robustness in Federated Learning With Stragglers and Adversaries

Abstract

Despite significant progress in federated learning (FL), persistent challenges, such as stragglers, adversaries, and communication costs remain. To address these issues, we propose FedLSC, a novel FL framework that leverages layer-selected correlation (LSC) to enhance both robustness and efficiency. In contrast to the existing methods, FedLSC does not rely on public data during model training, making it more practical and resilient in real-world scenarios. FedLSC introduces three key innovations: 1) preprocessing of layer selection (LS), which identifies significant layers to reduce communication costs and performance degradation; 2) local updates using LS-based scaled sign-stochastic gradient descent (SSS), introducing a layer-specific scaling mechanism to mitigate performance loss from quantization and significantly reduce communication costs; and 3) model aggregation via LSC-based schemes, which enhances robustness by processing only the significant layers and mitigating the impact of stragglers and adversaries. Furthermore, integrating the SSS scheme into FedLSC reduces communication costs to as little as 0.01% of those in state-of-the-art (SOTA) method while maintaining performance. Evaluations conducted across various FL scenarios show that FedLSC effectively supports robust performance and efficiency, even in bandwidth-constrained environments, thereby confirming its practicality in modern FL applications.