AI RESEARCH
Computation and Communication Efficient Federated Unlearning via On-server Gradient Conflict Mitigation and Expression
arXiv CS.AI
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ArXi:2603.13795v1 Announce Type: cross Federated Unlearning (FUL) aims to remove specific participants' data contributions from a trained Federated Learning model, thereby ensuring data privacy and compliance with regulatory requirements. Despite its potential, progress in FUL has been limited due to several challenges, including the cross-client knowledge inaccessibility and high computational and communication costs. To overcome these challenges, we propose Federated On-server Unlearning (FOUL), a novel framework that comprises two key stages.