面向移动自主系统的生活周期感知联邦持续学习
Lifecycle-Aware Federated Continual Learning in Mobile Autonomous Systems
AI总结：
本文提出一种生活周期感知的双时间尺度联邦持续学习框架，通过分层选择性复习策略和快速知识恢复策略，解决联邦持续学习中的遗忘问题，并在真实场景中验证了其有效性。
摘要：
联邦持续学习（FCL）使分布式自主舰队能够协同适应不断变化的地形类型，但现有方法面临三大挑战：1）使用统一保护策略，未考虑不同网络层对遗忘的敏感性差异；2）主要关注训练期间的遗忘防止，未解决长期累积漂移的长期影响；3）依赖理想化模拟，无法捕捉分布式舰队中的现实异质性。本文提出一种生活周期感知的双时间尺度FCL框架，结合训练时（预遗忘）防止和（后遗忘）恢复。在此框架下，我们设计了分层选择性复习策略，以减轻局部训练期间的即时遗忘，并设计了快速知识恢复策略，以在长期累积漂移后恢复退化模型。我们进行了理论分析，表征了异质遗忘动态并建立了长期退化的不可避免性。实验结果表明，该框架在最强的联邦基线上实现了高达8.3%的mIoU提升，在传统微调上实现了高达31.7%的提升。我们还将其部署在真实世界轮式机器人测试台上，以评估在现实约束下的系统级鲁棒性；测试结果进一步验证了我们的FCL设计的有效性。
英文摘要：
Federated continual learning (FCL) allows distributed autonomous fleets to adapt collaboratively to evolving terrain types across extended mission lifecycles. However, current approaches face several key challenges: 1) they use uniform protection strategies that do not account for the varying sensitivities to forgetting on different network layers; 2) they focus primarily on preventing forgetting during training, without addressing the long-term effects of cumulative drift; and 3) they often depend on idealized simulations that fail to capture the real-world heterogeneity present in distributed fleets. In this paper, we propose a lifecycle-aware dual-timescale FCL framework that incorporates training-time (pre-forgetting) prevention and (post-forgetting) recovery. Under this framework, we design a layer-selective rehearsal strategy that mitigates immediate forgetting during local training, and a rapid knowledge recovery strategy that restores degraded models after long-term cumulative drift. We present a theoretical analysis that characterizes heterogeneous forgetting dynamics and establishes the inevitability of long-term degradation. Our experimental results show that this framework achieves up to 8.3\% mIoU improvement over the strongest federated baseline and up to 31.7\% over conventional fine-tuning. We also deploy the FCL framework on a real-world rover testbed to assess system-level robustness under realistic constraints; the testing results further confirm the effectiveness of our FCL design.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
