An Inclusive and Lightweight Approach to Federated Continual Learning for Cultural Heritage
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Computer Science > Machine Learning
arXiv:2608.20038 (cs)
[Submitted on 20 Aug 2026]
Title:An Inclusive and Lightweight Approach to Federated Continual Learning for Cultural Heritage
Authors:Ioannis Theologitis, Debin Meng, Stylianos Eleftheriadis, Vasileios Lolis, Konstantinos Votis
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Abstract: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.
| Comments: | 7 pages, 3 figures, Accepted at the 2026 IEEE International Conference on Cyber Humanities (IEEE-CH 2026), Venice, Italy, September 7--9, 2026. Accepted author manuscript. Copyright 2026 IEEE |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2608.20038 [cs.LG] |
| (or arXiv:2608.20038v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.20038
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Ioannis Theologitis [view email][v1] Thu, 20 Aug 2026 13:44:29 UTC (2,668 KB)
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