轻量级联邦持续学习，防止文化遗产模型遗忘
An Inclusive and Lightweight Approach to Federated Continual Learning for Cultural Heritage
- 通过参数重要性累积保护知识，仅定期更新
- 在WikiArt上减少遗忘，提升公平与能效
- 适合资源有限的文化机构持续训练
人工智能可支持文化遗产与数字人文，通过大规模检索和分析数字化藏品。然而，文化遗产数据常分散于各机构，受所有权和访问限制，且随时间持续演化。联邦持续学习（FCL）适用于此场景，使模型能在不共享原始数据的前提下，从分布式和连续的数据中学习。本文提出FedCurv-DR，一种轻量级、基于正则化的FCL策略：通过累积客户端和经验中的参数重要性估计来保护已学知识，仅在固定周期内更新，以最小化通信与计算开销。我们在使用WikiArt图像数据集进行风格分类的持续学习场景中评估该方法，报告了性能、能耗与公平性指标。结果表明，FedCurv-DR有效减少遗忘，并在性能、公平性与能效之间取得平衡，实现可持续的文化遗产人工智能。
原文摘要 · Abstract (English)
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.
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