Federated Learning in Edge Computing: Vulnerabilities, Attacks, and Defenses—A Survey

Abstract

Federated Learning (FL), a distributed machine learning framework, enables collaborative model training across multiple devices without sharing raw data, thereby preserving privacy and reducing communication costs. When combined with Edge Computing (EC), FL brings computations closer to data sources, enabling low-latency, real-time decision-making in resource-constrained environments. However, this decentralization introduces several vulnerabilities, including data poisoning, backdoor attacks, inference leaks, and Byzantine behaviors, which are worsened by the heterogeneity of edge devices and their intermittent connectivity. This survey presents a comprehensive review of the intersection of FL and EC, focusing on vulnerabilities, attack vectors, and defense mechanisms. We analyze existing methods for robust aggregation, anomaly detection, differential privacy, and secure aggregation, with a focus on their feasibility within edge environments. Additionally, we identify open research challenges, such as scalability, resilience to heterogeneity, and energy-efficient defenses, and provide insights into the evolving landscape of FL in edge computing. This review aims to inform future research on enhancing the security, privacy, and efficiency of FL systems deployed in real-world edge environments.