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

arXiv:2608.20038v1 Announce Type: cross 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.

Related

• Continual Learning with Elastic Regularization and Synthetic Replay for Federated MLLM Fine-Tuning
• ArtiFact: A Large-Scale Multi-Modal Cultural Heritage Dataset
• JumpLoRA: Sparse Adapters for Continual Learning in Large Language Models
• WhiteTesseract: Reframing the Interpretation of Cultural Heritage through XR and Conversational AI

Source: arXiv cs.AI | 2026-08-21