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 repair. The data to be deleted may have already shaped later retained trajectories, so retraining or model-side unlearning leaves an influence echo that returns as the network keeps learning. We propose Muting Unlearned Trajectories' Echoes (MUTE) for reliable deletion over edge networks. MUTE traces the echo from a lightweight ledger at the edge server, without moving raw trajectories off the de...
| Authors | Zihao Ding, Jun Huang, Liang Dong |
|---|---|
| Primary category | cs.NI |
| Published | 30 July 2026 |
| Last updated at source | 13 October 2026 |
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