A Review of Federated Learning Attacks: Threat Models and Defence Strategies
Fizlin Zakaria, Shamsul KamalAhmad Khalid · International Journal of Advanced Computer Science and Applications · 2025
Federated Learning (FL) has emerged as a critical paradigm in privacy-preserving machine learning, enabling collaborative model training across decentralised devices without sharing raw data. While FL enhances privacy by maintaining data locality, it remains susceptible to sophisticated adversarial attacks. This review systematically analyses the FL threat landscape and introduces a novel taxonomy that classifies attack models based on their objectives, capabilities, and exploited vulnerabilities. Major categories include data poisoning, inference attacks, and Byzantine behaviours, each examined in terms of mechanisms, assumptions, and system impact. In addition, the paper evaluates prominent defence strategies—such as differential privacy, secure aggregation, and anomaly detection—by assessing their strengths, limitations, and real-world applicability. Key gaps include the lack of standardised evaluation metrics and limited exploration of adaptive defence mechanisms. Emerging trends such as homomorphic encryption, secure multi-party computation, and blockchain-based verifiability are also discussed. This review is a comprehensive resource for researchers and practitioners aiming to design resilient, privacy-aware FL systems that withstand evolving threats.