Causal Unlearning in Collaborative Optimization: Exact and Approximate Influence Reversal under Adversarial Contributions
Researchers have developed a new method called HF-KCU to efficiently remove a client's data contribution from federated learning models, addressing the computational burden of retraining. This approach approximates the influence function using Krylov subspace iterations, significantly reducing complexity and speeding up the process. A causal weighting mechanism ensures that only clients affected by the data deletion are updated, preserving model quality and enhancing privacy restoration, as demonstrated by membership inference attack success rates matching a retrained model. AI
IMPACT Enables more efficient and privacy-preserving data deletion in federated learning systems.