Federated Unlearning
Date of Addition
22 March 2025
A process within Federated Learning environments that enables the removal of specific data contributions from trained models without requiring complete retraining. It allows participants to exercise "the right to be forgotten" or remove malicious contributions while preserving valuable knowledge.
Federated unlearning encompasses three primary objectives: sample unlearning (removing specific data samples), class unlearning (removing all samples of a certain class), and client unlearning (removing an entire client's contribution). Effective unlearning algorithms ensure that the unlearned model exhibits performance indistinguishable from a model trained without the removed data. This capability is particularly important in federated settings where data remains distributed across multiple organizations or devices, making traditional centralized unlearning approaches impractical.
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