发表机构
South Dakota State University; Baylor University（南达科他州立大学; 贝勒大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对自改进联邦智能体网络后训练时数据删除易出现影响回声的问题，提出MUTE方法，经实验验证其可在保障任务效用的同时降低行为泄漏且通信开销更低。
AI 中文摘要
自改进的联邦智能体网络在部署后会通过收集当前策略的新轨迹并反馈到后续轮次继续训练，这种闭环使数据删除比一次性模型修复更困难。当数据所有者请求删除时，目标数据可能已影响后续保留的轨迹，因此重新训练或模型侧遗忘可能会留下影响回声，在网络继续运行时重新出现。我们表明，这种回声在保留数据重新训练后仍会存在，会随遗忘形状的保留数据量增加而增长，且可从部署、收集和聚合记录中追踪。为解决此问题，我们提出MUTE，即自改进联邦智能体网络中可靠删除的遗忘轨迹回声抑制方法。MUTE通过轻量级服务器账本估计下游影响，通过遗忘-保留更新消除当前残留，通过隔离或降权控制高影响保留轨迹，并在上行链路预算下审计后续行为以安排额外擦除。在LIBERO数据集上使用两个视觉-语言-动作主干、三种删除粒度及基于Jetson的物理边缘测试平台的实验表明，MUTE在保持任务效用的同时，将行为泄漏和影响再生维持在较低水平，且通信量远低于全量重新训练。
英文摘要
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 retained trajectories, so retraining or model-side unlearning can leave an influence echo that returns as the network continues to operate. We show that this echo survives retained-data retraining, grows with the amount of forget-shaped retained data, and can be traced from deployment, collection, and aggregation records. To address this problem, we propose MUTE, a Muting Unlearned Trajectories' Echoes method for reliable deletion in self-improving federated agent networks. MUTE estimates downstream influence from a lightweight server ledger, removes the current residue through a forget-retain update, contains high-influence retained trajectories through quarantine or down-weighting, and audits later behavior to schedule additional erasure under an uplink budget. Experiments on LIBERO with two vision-language-action backbones, three deletion granularities, and a physical Jetson-based edge testbed show that MUTE keeps behavioral leakage and influence regeneration low while preserving task utility and using much less communication than full retraining.
