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FlexFed: Mitigating Catastrophic Forgetting in Heterogeneous Federated Learning in Pervasive Computing Environments

arXiv 2505.13576 ↗First posted 19 May 2025cs.LGcited by 0

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Abstract (arXiv)

Federated Learning (FL) enables collaborative model training while preserving privacy by allowing clients to share model updates instead of raw data. Pervasive computing environments (e.g., for Human Activity Recognition, HAR), which we focus on in this paper, are characterized by resource-constrained end devices, streaming sensor data and intermittent client participation. Variations in user behavior, common in HAR environments, often result in non-stationary data distributions. As such, existing FL approaches face challenges in HAR settings due to differing assumptions. The combined effects of HAR characteristics, namely heterogeneous data and intermittent participation, can lead to a severe issue called catastrophic forgetting (CF). Unlike Continuous Learning (CL), which addresses CF using memory and replay mechanisms, FL's privacy constraints prohibit such strategies. To tackle CF in HAR environments, we propose FlexFed, a novel FL approach that prioritizes data retention for efficient memory use and dynamically adjusts offline training frequency based on distribution shifts, client capability and offline duration. To better quantify CF in FL, we introduce a new metric that accounts for under-represented data, enabling more accurate evaluations. We also develop a realistic HAR-based evaluation framework that simulates streaming data, dynamic distributions, imbalances and varying availability. Experiments show that FlexFed mitigates CF more effectively, improves FL efficiency by 10 to 15 % and achieves faster, more stable convergence, especially for infrequent or under-represented data.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 28 of its 43 references that are in the map. First posted on arXiv 19 May 2025.

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28 of 43 references.

References · 28

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019Advances and Open Problems in Federated Learningcited by 3,046
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
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  5. 2018Federated Learning with Non-IID Datacited by 1,108
  6. 2021Model-Contrastive Federated Learningcited by 766
  7. 2018Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigencecited by 742
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  9. 2020Federated Learning with Matched Averagingcited by 564
  10. 2021Federated Learning Based on Dynamic Regularizationcited by 562
  11. 2019FedMD: Heterogenous Federated Learning via Model Distillationcited by 459
  12. 2021Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learningcited by 193
16 more
  1. 2020FedBE: Making Bayesian Model Ensemble Applicable to Federated Learningcited by 176
  2. 2021FedScale: Benchmarking Model and System Performance of Federated Learning at Scalecited by 136
  3. 2019Overcoming Forgetting in Federated Learning on Non-IID Datacited by 120
  4. 2021Preservation of the Global Knowledge by Not-True Distillation in Federated Learningcited by 87
  5. 2021Fast Federated Learning in the Presence of Arbitrary Device Unavailabilitycited by 72
  6. 2023Better Generative Replay for Continual Federated Learningcited by 55
  7. 2022Acceleration of Federated Learning with Alleviated Forgetting in Local Trainingcited by 29
  8. 2023GradMA: A Gradient-Memory-based Accelerated Federated Learning with Alleviated Catastrophic Forgettingcited by 27
  9. 2021Resource-Efficient Federated Learningcited by 26
  10. 2023Federated Learning for Data Streamscited by 16
  11. 2023DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning with Static and Streaming Datasetcited by 14
  12. 2023On the Local Cache Update Rules in Streaming Federated Learningcited by 8
  13. 2022To Store or Not? Online Data Selection for Federated Learning with Limited Storagecited by 7
  14. 2022Addressing Client Drift in Federated Continual Learning with Adaptive Optimizationcited by 5
  15. 2024Flashback: Understanding and Mitigating Forgetting in Federated Learningcited by 4
  16. 2023A Framework for Realistic Simulation of Daily Human Activitycited by 2

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