发表机构
Information Technologies Institute, Centre for Research and Technology Hellas; School of Electronic Engineering and Computer Science, Queen Mary University of London（希腊研究与技术中心信息技术研究所; 伦敦大学玛丽女王学院电子工程与计算机科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对文化遗产数据分布受限且时序演变的问题，提出轻量级联邦持续学习策略FedCurv-DR，经WikiArt数据集评估可减轻遗忘，平衡性能、公平性与能效。
AI 中文摘要
人工智能可通过大规模检索和分析数字化藏品为文化遗产与数字人文提供支持。然而，文化遗产数据常分布于不同机构，受所有权与访问限制约束，且随时间不断演变。联邦持续学习（FCL）非常适配此类场景，因为它能让模型在不共享原始藏品的情况下从分布式且时序变化的数据中学习。本文提出FedCurv-DR，一种基于正则化的轻量级联邦持续学习策略。该方法在各客户端及各轮次中累积参数重要性估计以保护已学习的知识，同时仅在固定间隔更新参数，以最小化通信与计算开销。我们在持续学习场景中，使用WikiArt图像数据集进行风格演变下的流派分类任务来评估FedCurv-DR，报告了性能、能耗与公平性指标。结果表明，FedCurv-DR可减轻遗忘问题，并在文化遗产领域的可持续人工智能中平衡性能、公平性与能效。
英文摘要
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.
Comments7 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
