PFL-LDG: Privacy-preserving Federated Learning via Lightweight Device Grouping

Abstract

The rapid growth of private data from distributed edge networks, driven by the proliferation of IoT sensors, wearable devices, and smartphones, offers significant opportunities for AI applications. However, traditional distributed machine learning methods struggle to address data privacy concerns effectively. Federated learning (FL) has appeared as a popular, innovative paradigm for distributed machine learning that enables collaborative training of models across multiple data silos while preserving privacy. Yet, in large-scale and complex edge networks, the convergence performance of existing FL methods deteriorates when dealing with highly heterogeneous data. This paper introduces PFL-LDG, a similarity-based lightweight privacy-protected grouping FL method that mitigates the impact of non-IID data on FL model performance in dataheterogeneous scenarios. Unlike conventional FL, PFL-LDG clusters devices based on data distribution similarity, reducing inefficiency and straggler issues while supplying personalized FL models for edge devices and enhancing FL accuracy. The paper's main contribution is the proposal of a novel similaritybased lightweight privacy-protected grouping FL framework, focusing on improving privacy protection and training efficiency in heterogeneous edge resource-constrained FL systems.