{"id":11611,"date":"2026-09-06T15:26:33","date_gmt":"2026-09-06T07:26:33","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=11611"},"modified":"2026-09-06T16:55:54","modified_gmt":"2026-09-06T08:55:54","slug":"jase-202612-35-020","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202612-35-020","title":{"rendered":"A Federated Learning Approach for Cross-Institutional Intangible Cultural Heritage Multimedia Content Retrieval and Sensitive Information Protection in the Digital Cultural and Creative Industry"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 35<\/a><\/p>\n<\/div>\n\n\n\n

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Qiku Bao<\/i><\/a><\/p>\n\n\n\n

Jilin Animation Institute, 130000, Jilin, China<\/p>\n<\/div>\n\n\n\n

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Received: July 21, 2026
Accepted: August 20, 2026
Publication Date: September 06, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

Convergence Curves of Different Federated Retrieval Methods <\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation: BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202612_35.020<\/a> <\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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In the digital cultural and creative industry, ICH multimedia resources (images, videos, audio, text, metadata) are scattered across cultural institutions and often involve sensitive information (identity, location, craft knowledge, ritual scenes), so centralized retrieval cannot balance sharing, efficiency, and privacy. This study proposes a federated learning framework combining local multimodal encoding, federated contrastive learning for cross-modal alignment, gradient pruning\/DP\/secure aggregation against inference risks, and retrieval combining prototype routing, local indexing, sensitivity assessment, and result anonymization for privacy-protected Top-K retrieval. On 31,500 cross-institutional non-IID ICH samples, the method achieves Recall@5 of 0.807, mAP of 0.752, and NDCG@10 of 0.793 (beating FedAvg, FedProx, FedCMR, FedCMR-DP), maintains Recall@5 of 0.738 at non-IID degree 0.9, reduces sensitive-information exposure from 18.6% to 4.1%, lowers member\/attribute-inference success to 38.6%\/33.7%, and achieves 126 ms latency and 472 queries\/s throughput at 20 nodes.<\/p>\n\n\n\n

Keywords: federated learning; intangible cultural heritage; multimedia retrieval; sensitive information protection; distributed index; scalable information system; differential privacy<\/em><\/p>\n\n\n\n

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