Mitigating Poisoning Threats and Their Privacy Implications in Federated Learning
Abstract
Advancements in Machine Learning (ML) and distributed intelligence have accelerated the adoption of Federated Learning (FL) as a key enabler of privacy-preserving applications in domains such as IoT, healthcare, and Beyond 5G networks. FL enables collaborative model training without centralising data, but its distributed and heterogeneous nature introduces new security and privacy challenges. In addition to conventional adversarial threats, FL faces unique vulnerabilities including poisoning attacks, and privacy leakage through model updates via threats like inference attacks. Addressing these challenges is critical for ensuring trust in next-generation distributed AI systems. This thesis investigates such threats in FL with a particular emphasis on poisoning attacks and the interplay between poisoning and privacy leakage. The work also investigates how poisoning attacks can be mitigated, essentially preventing any privacy vulnerabilities induced via poisoning. To this end, four major contributions are presented: Rec-Def, SHERPA, X-SHIELD, and ROAM. First, Rec-Def is proposed as a defence against GAN-based inference attacks, highlighting the connection between poisoning and privacy risks. Second, SHERPA introduces an interpretable aggregation algorithm that employs Explainable AI (XAI) technique called SHAP feature attributions to detect poisoned model updates, improving the transparency and reliability of poisoning detection. Third, X-SHIELD extends this approach to…
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