Title: Federated learning: frameworks, optimisation algorithms, security threats and defences

Authors: HongYun Cai; Yu Zhang; ShiYun Wang; MeiLing Zhang; Ao Zhao

Addresses: School of Cyberspace Security and Computer Science, Hebei University, HBU, China ' School of Cyberspace Security and Computer Science, Hebei University, HBU, China ' School of Cyberspace Security and Computer Science, Hebei University, HBU, China ' School of Cyberspace Security and Computer Science, Hebei University, HBU, China ' School of Cyberspace Security and Computer Science, Hebei University, HBU, China

Abstract: Federated learning (FL) integrates dispersed data across various locations and performs modelling and analysis directly on edge devices. However, as an emerging field, it faces significant security and privacy challenges. This paper provides a comprehensive review of the security threats and defences in FL. We present an in-depth overview of the FL framework, optimisation algorithms, current security threats, and corresponding defence mechanisms, along with a discussion of the difficulties and challenges encountered. Our findings indicate that the primary security threats compromise the privacy and robustness of FL, which are the critical issues that defences must address. We conclude by proposing important future research directions to enhance FL's adaptability to diverse environmental requirements.

Keywords: federated learning; security threat; security defence; privacy; robustness.

DOI: 10.1504/IJICS.2025.149454

International Journal of Information and Computer Security, 2025 Vol.28 No.3, pp.265 - 303

Accepted: 16 Oct 2024
Published online: 31 Oct 2025 *

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