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- research-articleOctober 2024
Robust Federated Unlearning
CIKM '24: Proceedings of the 33rd ACM International Conference on Information and Knowledge ManagementPages 2034–2044https://doi.org/10.1145/3627673.3679817Federated unlearning (FU) algorithms offer participants in federated learning (FL) the "right to be forgotten'' for their individual data and its impact on a collaboratively trained model. Existing FU algorithms primarily focus on accelerating the ...
- research-articleOctober 2024
Breaking State-of-the-Art Poisoning Defenses to Federated Learning: An Optimization-Based Attack Framework
CIKM '24: Proceedings of the 33rd ACM International Conference on Information and Knowledge ManagementPages 2930–2939https://doi.org/10.1145/3627673.3679566Federated Learning (FL) is a novel client-server distributed learning framework that can protect data privacy. However, recent works show that FL is vulnerable to poisoning attacks. Many defenses with robust aggregators (AGRs) are proposed to mitigate ...