MLFCIL: A Multi-Level Forgetting Mitigation Framework for Federated Class-Incremental Learning in LEO Satellites

• URL: http://arxiv.org/abs/2604.02356v1
• Date: Sat, 14 Mar 2026 12:44:36 GMT
• Title: MLFCIL: A Multi-Level Forgetting Mitigation Framework for Federated Class-Incremental Learning in LEO Satellites
• Abstract summary: Federated class-incremental learning (FCIL) enables distributed incremental learning without sharing raw data.<n>We propose MLFCIL, a multi-level forgetting mitigation framework that decomposes catastrophic forgetting into three sources.<n>MLFCIL significantly outperforms baselines in both accuracy and forgetting mitigation, while introducing minimal resource overhead.
• Score: 8.612785005549906
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Low-Earth-orbit (LEO) satellite constellations are increasingly performing on-board computing. However, the continuous emergence of new classes under strict memory and communication constraints poses major challenges for collaborative training. Federated class-incremental learning (FCIL) enables distributed incremental learning without sharing raw data, but faces three LEO-specific challenges: non-independent and identically distributed data heterogeneity caused by orbital dynamics, amplified catastrophic forgetting during aggregation, and the need to balance stability and plasticity under limited resources. To tackle these challenges, we propose MLFCIL, a multi-level forgetting mitigation framework that decomposes catastrophic forgetting into three sources and addresses them at different levels: class-reweighted loss to reduce local bias, knowledge distillation with feature replay and prototype-guided drift compensation to preserve cross-task knowledge, and class-aware aggregation to mitigate forgetting during federation. In addition, we design a dual-granularity coordination strategy that combines round-level adaptive loss balancing with step-level gradient projection to further enhance the stability-plasticity trade-off. Experiments on the NWPU-RESISC45 dataset show that MLFCIL significantly outperforms baselines in both accuracy and forgetting mitigation, while introducing minimal resource overhead.

Related papers

• Hybrid Imbalanced Regression Through Unified Data-Level and Algorithm-Level Balancing [1.3700362496838856]
  Imbalanced learning is a critical challenge in machine learning, where underrepresented target values can bias models and degrade prediction performance on rare but important cases.<n>We propose a unified hybrid framework that integrates both data- and algorithm-level balancing strategies into a regressor-agnostic pipeline.<n> Experimental results on benchmark datasets demonstrate that the proposed framework consistently improves predictive performance compared to standalone regressors and existing imbalanced regression approaches.
  arXiv  Detail & Related papers  (2026-05-31T13:13:10Z)
• Janus-LoRA: A Balanced Low-Rank Adaptation for Continual Learning [122.06915301391616]
  Low-Rank Adaptation (LoRA) has emerged as a promising paradigm for Continual Learning.<n>We propose textbfJanus-LoRA, a framework that restores this balance through two novel components.
  arXiv  Detail & Related papers  (2026-05-27T13:53:14Z)
• Enhancing Gradient Inversion Attacks in Federated Learning via Hierarchical Feature Optimization [56.95448807869383]
  Federated Learning (FL) has emerged as a compelling paradigm for privacy-preserving distributed machine learning.<n>Recent studies find that the gradients exchanged in the FL system are also vulnerable to privacy leakage.<n>We propose textbfGradient textbfInversion over textbfFeature textbfDomains (GIFD)
  arXiv  Detail & Related papers  (2026-04-01T14:32:15Z)
• Multi-AUV Cooperative Target Tracking Based on Supervised Diffusion-Aided Multi-Agent Reinforcement Learning [19.584329065489893]
  This paper proposes a hierarchical MARL architecture comprising four layers: global training scheduling, multi-agent coordination, local decision-making, and real-time execution.<n>We propose the Supervised Diffusion-Aided MARL algorithm featuring three innovations: 1) a dual-decision architecture with segregated experience pools mitigating nonstationarity through structured experience replay; 2) a supervised learning mechanism guiding the diffusion model's reverse denoising process to generate high-fidelity training samples that accelerate convergence; and 3) disturbance-robust policy learning incorporating behavioral cloning loss to guide the Deep Deterministic Policy Gradient network update using high-quality replay actions, eliminating
  arXiv  Detail & Related papers  (2026-03-31T08:32:32Z)
• Resilient Class-Incremental Learning: on the Interplay of Drifting, Unlabelled and Imbalanced Data Streams [3.3977968101293956]
  This paper proposes SCIL (Streaming Class-Incremental Learning) to address these challenges.<n>The framework integrates an autoencoder (AE) with a multi-layer perceptron for multi-class prediction, uses a dual-loss strategy (classification and reconstruction) for prediction and new class detection, employs corrected pseudo-labels for online training, manages classes with queues, and applies oversampling to handle imbalance.<n>Our results demonstrate that SCIL outperforms strong baselines and state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-02-10T11:37:39Z)
• Consolidation or Adaptation? PRISM: Disentangling SFT and RL Data via Gradient Concentration [56.074760766965085]
  PRISM achieves a dynamics-aware framework that arbitrates data based on its degree of cognitive conflict with the model's existing knowledge.<n>Our findings suggest that disentangling data based on internal optimization regimes is crucial for scalable and robust agent alignment.
  arXiv  Detail & Related papers  (2026-01-12T05:43:20Z)
• Unbiased Max-Min Embedding Classification for Transductive Few-Shot Learning: Clustering and Classification Are All You Need [83.10178754323955]
  Few-shot learning enables models to generalize from only a few labeled examples.<n>We propose the Unbiased Max-Min Embedding Classification (UMMEC) Method, which addresses the key challenges in few-shot learning.<n>Our method significantly improves classification performance with minimal labeled data, advancing the state-of-the-art in annotatedL.
  arXiv  Detail & Related papers  (2025-03-28T07:23:07Z)
• Continuous Knowledge-Preserving Decomposition with Adaptive Layer Selection for Few-Shot Class-Incremental Learning [73.59672160329296]
  CKPD-FSCIL is a unified framework that unlocks the underutilized capacity of pretrained weights.<n>Our method consistently outperforms state-of-the-art approaches in both adaptability and knowledge retention.
  arXiv  Detail & Related papers  (2025-01-09T07:18:48Z)
• TriplePlay: Enhancing Federated Learning with CLIP for Non-IID Data and Resource Efficiency [0.0]
  TriplePlay is a framework that integrates CLIP as an adapter to enhance FL's adaptability and performance across diverse data distributions. Our simulation results demonstrate that TriplePlay effectively decreases GPU usage costs and speeds up the learning process, achieving convergence with reduced communication overhead.
  arXiv  Detail & Related papers  (2024-09-09T06:04:42Z)
• FedSat: A Statistical Aggregation Approach for Class Imbalanced Clients in Federated Learning [2.5628953713168685]
  Federated learning (FL) has emerged as a promising paradigm for privacy-preserving distributed machine learning.<n>This paper presents FedSat, a novel FL approach specifically designed to handle three forms of data heterogeneity, namely label skewness, missing classes, and quantity skewness.<n>Experiments across diverse data-heterogeneity settings demonstrate that FedSat significantly outperforms state-of-the-art baselines.
  arXiv  Detail & Related papers  (2024-07-04T11:50:24Z)
• OrCo: Towards Better Generalization via Orthogonality and Contrast for Few-Shot Class-Incremental Learning [57.43911113915546]
  Few-Shot Class-Incremental Learning (FSCIL) introduces a paradigm in which the problem space expands with limited data. FSCIL methods inherently face the challenge of catastrophic forgetting as data arrives incrementally. We propose the OrCo framework built on two core principles: features' orthogonality in the representation space, and contrastive learning.
  arXiv  Detail & Related papers  (2024-03-27T13:30:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.