Federated Learning Approaches for Secure Edge Computing

Abstract

Federated Learning (FL) has become one of the paradigms shifting towards machine learning, allowing a number of edge devices to jointly train models without exchanging raw data. This can be used to guarantee privacy of the data, less data communication overhead, and real-time decision-making under edge computing conditions. The paper discusses federated learning to secure edge computing, focusing on architecture, communication models, aggregation, and security. The experimental and theoretical studies show that FL is capable of reaching model accuracy that is similar to the centralized learning and reducing privacy threats and adversarial risks. The problems, including the heterogeneity of data, and the lack of computing power and communication efficiency, are discussed critically. The research paper ends with the suggestions on how to improve FL implementation in the actual edge networks, focusing on secure aggregation protocols, differential privacy, and client selection adaption strategies.