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:
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(i) a threat-centric view of robustness that categorizes the multifaceted attack surfaces,
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(ii) a structured taxonomy of robust aggregation strategies distinguishing outcome-centric approaches from security-centric strategies,
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(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.