Lifecycle-Aware Federated Continual Learning in Mobile Autonomous Systems

TL;DR

This paper introduces a lifecycle-aware federated continual learning framework for mobile autonomous systems, addressing long-term forgetting and heterogeneity in real-world deployments.

Contribution

It proposes a dual-timescale FCL framework with layer-selective rehearsal and rapid recovery strategies, validated through theoretical analysis and real-world rover tests.

Findings

Achieves up to 8.3% mIoU improvement over federated baselines.

Up to 31.7% improvement over conventional fine-tuning.

Demonstrates robustness in real-world rover deployment.

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.