ConDa: Fast Federated Unlearning with Contribution Dampening
Keywords: federated unlearning, machine unlearning, fast unlearning, non-IID federated unlearning
TL;DR: ConDa is the most efficient federated unlearning method and focuses on non-IID federated learning setup which is extremely challenging
Abstract: Federated learning (FL) has enabled collaborative model training across decentralized data sources or clients. While adding new participants to a shared model does not pose great technical hurdles, the removal of a participant and their related information contained in the shared model remains a challenge. To address this problem, federated unlearning has emerged as a critical research direction, seeking to remove information from globally trained models without harming the model performance on the remaining data. Most modern federated unlearning methods use costly approaches such as the use of remaining clients data to retrain the global model or methods that would require heavy computation on client or server side. We introduce Contribution Dampening (\textsc{ConDa}), a framework that performs efficient unlearning by tracking down the parameters which affect the global model for each client and performs synaptic dampening on the parameters of the global model that have privacy infringing contributions from the forgetting client. Our technique does not require clients data or any kind of retraining and it does not put any computational overhead on either the client or server side. We perform experiments on multiple datasets and demonstrate that \textsc{ConDa} is effective to forget a client’s data. In experiments conducted on the MNIST, CIFAR10, and CIFAR100 datasets, \textsc{ConDa} proves to be the fastest federated unlearning method, outperforming the nearest state-of-the-art approach by at least 100×. Our emphasis is on the non-IID Federated Learning setting, which presents the greatest challenge for unlearning. Additionally, we validate \textsc{ConDa}'s robustness through backdoor and membership inference attacks. We envision this work as a crucial component for FL in adhering to legal and ethical requirements.
Primary Area: alignment, fairness, safety, privacy, and societal considerations
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Submission Number: 992
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