Liang Dong

When Unlearning Fails: Reliable Data Deletion under Post-Training in Agent Networks

Self-improving federated agent networks keep training after deployment by collecting new trajectories with the current policy and feeding them back into later rounds. This closed loop makes unlearning harder than a one-time model repair. When a data owner requests deletion, the target data may have already shaped later...