# Lifecycle-Aware Federated Continual Learning in Mobile Autonomous Systems

arXiv: https://arxiv.org/abs/2604.20745 · HTML: https://mlsanity.com/paper/2604.20745/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 60 references that are in the map.
- First posted on arXiv 22 Apr 2026.

## Builds on (most cited first, 30 of 32)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [iCaRL: Incremental Classifier and Representation Learning](https://mlsanity.com/paper/1611.07725/) (2016)
- [Dark Experience for General Continual Learning: a Strong, Simple Baseline](https://mlsanity.com/paper/2004.07211/) (2020)
- [A Comprehensive Survey of Continual Learning: Theory, Method and Application](https://mlsanity.com/paper/2302.00487/) (2023)
- [PLOP: Learning without Forgetting for Continual Semantic Segmentation](https://mlsanity.com/paper/2011.11390/) (2020)
- [Nested Learning: The Illusion of Deep Learning Architectures](https://mlsanity.com/paper/2512.24695/) (2025)
- [Federated Continual Learning via Knowledge Fusion: A Survey](https://mlsanity.com/paper/2312.16475/) (2023)
- [Federated Incremental Semantic Segmentation](https://mlsanity.com/paper/2304.04620/) (2023)
- [CL-LoRA: Continual Low-Rank Adaptation for Rehearsal-Free Class-Incremental Learning](https://mlsanity.com/paper/2505.24816/) (2025)
- [Online-LoRA: Task-free Online Continual Learning via Low Rank Adaptation](https://mlsanity.com/paper/2411.05663/) (2024)
- [Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey](https://mlsanity.com/paper/2412.13840/) (2024)
- [Does Continual Learning Equally Forget All Parameters?](https://mlsanity.com/paper/2304.04158/) (2023)
- [Prioritized Information Bottleneck Theoretic Framework with Distributed Online Learning for Edge Video Analytics](https://mlsanity.com/paper/2409.00146/) (2024)
- [R-ACP: Real-Time Adaptive Collaborative Perception Leveraging Robust Task-Oriented Communications](https://mlsanity.com/paper/2410.04168/) (2024)
- [Forgetting, Ignorance or Myopia: Revisiting Key Challenges in Online Continual Learning](https://mlsanity.com/paper/2409.19245/) (2024)
- [EVCL: Elastic Variational Continual Learning with Weight Consolidation](https://mlsanity.com/paper/2406.15972/) (2024)
- [Enhancing Vehicular Platooning with Wireless Federated Learning: A Resource-Aware Control Framework](https://mlsanity.com/paper/2507.00856/) (2025)
- [Cs2K: Class-specific and Class-shared Knowledge Guidance for Incremental Semantic Segmentation](https://mlsanity.com/paper/2407.09047/) (2024)
- ["X of Information'' Continuum: A Survey on AI-Driven Multi-dimensional Metrics for Next-Generation Networked Systems](https://mlsanity.com/paper/2507.19657/) (2025)
- [Shared Spatial Memory Through Predictive Coding](https://mlsanity.com/paper/2511.04235/) (2025)
- [RELIEF: Turning Missing Modalities into Training Acceleration for Federated Learning on Heterogeneous IoT Edge](https://mlsanity.com/paper/2604.04243/) (2026)
- [S5Mars: Semi-Supervised Learning for Mars Semantic Segmentation](https://mlsanity.com/paper/2207.01200/) (2022)
- [Beyond Background Shift: Rethinking Instance Replay in Continual Semantic Segmentation](https://mlsanity.com/paper/2503.22136/) (2025)
- [CoMBO: Conflict Mitigation via Branched Optimization for Class Incremental Segmentation](https://mlsanity.com/paper/2504.04156/) (2025)
- [Reinforcement Learning-Based Energy-Aware Coverage Path Planning for Precision Agriculture](https://mlsanity.com/paper/2601.16405/) (2026)

## Built on by (most cited first, 7 of 7)

- [Inference-Time Budget Control for LLM Search Agents](https://mlsanity.com/paper/2605.05701/) (2026)
- [Forget to Improve: On-Device LLM-Agent Continual Learning via Budget-Curated Memory](https://mlsanity.com/paper/2606.25115/) (2026)
- [EASE: Federated Multimodal Unlearning via Entanglement-Aware Anchor Closure](https://mlsanity.com/paper/2605.00733/) (2026)
- [CrystalMem: Elastic Memory for Self-Evolving LLM Agents via Knowledge Crystallization](https://mlsanity.com/paper/2608.00303/) (2026)
- [ERRAND: Budgeted Maintenance of Agent Memory](https://mlsanity.com/paper/2609.29545/) (2026)
- [When Unlearning Fails: Reliable Data Deletion under Post-Training in Agent Networks](https://mlsanity.com/paper/2607.28829/) (2026)
- [Not All Experience Belongs in the Weights: Component Routing for Self-Improving GUI Agents](https://mlsanity.com/paper/2610.01787/) (2026)

## Abstract (arXiv)

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.