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When Unlearning Fails: Reliable Data Deletion under Post-Training in Agent Networks
Embedded AI agents on edge devices can learn as a federation and keep improving after deployment. Each agent collects new trajectories with the current policy, and the edge server aggregates them in later rounds. In such self-improving agent networks, deleting data is harder than a one-time model re
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