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An Inclusive and Lightweight Approach to Federated Continual Learning for Cultural Heritage

arXiv 2608.20038 ↗First posted 20 Aug 2026cs.LG cs.AI cs.CVcited by 0

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Abstract (arXiv)

Artificial intelligence can support cultural heritage and digital humanities through large-scale retrieval and analysis of digitized collections. However, cultural heritage data are often distributed across institutions, constrained by ownership and access restrictions, and continuously evolving over time. Federated Continual Learning (FCL) is well suited to this setting, as it enables models to learn from distributed and sequential data without sharing raw collections. In this paper, we propose FedCurv-DR, a lightweight, regularisation-based FCL strategy. The method accumulates parameter-importance estimates across clients and experiences to protect learned knowledge, while updating them only at fixed intervals to minimize communication and computation overhead. We evaluate FedCurv-DR in a continual learning scenario using the WikiArt image dataset for genre classification with evolving styles, reporting performance, energy, and fairness metrics. Our results show that FedCurv- DR reduces forgetting and balances performance, fairness, and energy efficiency for sustainable AI in cultural heritage.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 37 references that are in the map. First posted on arXiv 20 Aug 2026.

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References · 21

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2016Overcoming catastrophic forgetting in neural networkscited by 4,787
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  4. 2016Deep Learning with Differential Privacycited by 2,728
  5. 2017Gradient Episodic Memory for Continual Learningcited by 1,898
  6. 2017Memory Aware Synapses: Learning what (not) to forgetcited by 1,105
  7. 2018Experience Replay for Continual Learningcited by 1,095
  8. 2020Ensemble Distillation for Robust Model Fusion in Federated Learningcited by 682
  9. 2019Green AIcited by 627
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  11. 2019Fair Resource Allocation in Federated Learningcited by 394
  12. 2016Practical Secure Aggregation for Federated Learning on User-Held Datacited by 178
9 more
  1. 2020Federated Continual Learning with Weighted Inter-client Transfercited by 161
  2. 2021Avalanche: an End-to-End Library for Continual Learningcited by 124
  3. 2019Overcoming Forgetting in Federated Learning on Non-IID Datacited by 120
  4. 2021FairFed: Enabling Group Fairness in Federated Learningcited by 118
  5. 2023TARGET: Federated Class-Continual Learning via Exemplar-Free Distillationcited by 65
  6. 2020Continual Local Training for Better Initialization of Federated Modelscited by 34
  7. 2022Federated Continual Learning through distillation in pervasive computingcited by 10
  8. 2026Elastic Weight Consolidation Done Right for Continual Learningcited by 3
  9. 2025Towards a Manifesto for Cyber Humanities: Paradigms, Ethics, and Prospectscited by 2

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