Lifecycle-Aware Federated Continual Learning in Mobile Autonomous Systems

2026-04-22

AI Summary

This work addresses the challenges of device heterogeneity, varying layer-wise susceptibility to catastrophic forgetting, and long-term distribution drift in federated continual learning for mobile autonomous systems. The authors propose a lifespan-aware, dual-timescale federated continual learning framework that introduces layer-selective replay and a post-deployment rapid knowledge recovery mechanism, jointly mitigating forgetting during both training and inference. Theoretical analysis reveals heterogeneous forgetting dynamics across network layers and demonstrates that long-term performance degradation is inevitable under conventional assumptions, thereby overcoming the limitation of prior methods that focus solely on the training phase. Evaluated on a real Mars rover platform, the proposed approach achieves an 8.3% improvement in mIoU over the strongest federated baseline and a 31.7% gain over standard fine-tuning, significantly enhancing stability and adaptability in long-horizon tasks.

Technology Category

• Machine Learning: Life-Long and Continual Learning
• Multiagent Systems: Multiagent Learning
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• federated continual learning
• catastrophic forgetting
• cumulative drift
• heterogeneous fleets
• lifecycle-aware learning

Innovation

• federated continual learning
• lifecycle-aware
• layer-selective rehearsal
• knowledge recovery
• cumulative drift