SPIN Unprocessed October 27, 2026 ai_technology research
Not All Retained Clients Are Equal: Client Selection for Federated Unlearning
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arXiv:2610.31786v1 Announce Type: new Abstract: Federated unlearning (FU) aims to remove a designated target client's influence from a trained model without the prohibitive cost of retraining from scratch. Existing methods largely treat unlearning as a post-hoc, stateless operation, overlooking that client influence is path-dependent: contributions made during training, particularly during critical learning periods (CLPs), can have disproportionate and persistent effects. Moreover, knowledge ove
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