Upholding Robustness in Federated Learning: Trends, Emerging Strategies, and Research Opportunities

Abstract:While Federated Learning (FL) has been widely adopted for protecting user privacy in machine learning, it remains vulnerable to various robustness challenges, including performance-impairment risks, information-stealing threats, and aggregation vulnerabilities. This work offers a holistic synthesis of FL robustness along three tightly coupled angles: (i) a threat-centric view of robustness that categorizes the multifaceted attack surfaces, (ii) a structured taxonomy of robust aggregation strategies distinguishing outcome-centric approaches from security-centric strategies, and (iii) a layered taxonomy of defensive strategies. We rigorously examine current evaluation practices for FL robustness and identify major applications and open research challenges to guide future research.

Machine Learning (cs.LG)

arXiv:2609.28722 [cs.LG]

(or arXiv:2609.28722v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.28722 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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