{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:35:59Z","timestamp":1761176159877,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>Handling data staleness remains a significant challenge in federated learning with highly time-sensitive tasks, where data is generated continuously and data staleness largely affects model performance. Although recent works attempt to optimize data staleness by determining local data update frequency or client selection strategy, none of them explore taking both data staleness and data volume into consideration. In this paper, we propose Data Updating in Federated Learning (DUFL), an incentive mechanism featuring an innovative local data update scheme manipulated by three knobs: the server\u2019s payment, outdated data conservation rate, and clients\u2019 fresh data collection volume, to coordinate staleness and volume of local data for best utilities. To this end, we introduce a novel metric called Degree of Staleness (DoS) to quantify data staleness and conduct a theoretic analysis illustrating the quantitative relationship between DoS and model performance. We model DUFL as a two-stage Stackelberg game with dynamic constraint, deriving the optimal local data update strategy for each client in closed-form and the approximately optimal strategy for the server. Experimental results on real-world datasets demonstrate the significant performance of our approach.<\/jats:p>","DOI":"10.3233\/faia250958","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:47:08Z","timestamp":1761126428000},"source":"Crossref","is-referenced-by-count":0,"title":["Degree of Staleness-Aware Data Updating in Federated Learning"],"prefix":"10.3233","author":[{"given":"Tao","family":"Liu","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Sun Yat-sen University"}]},{"given":"Xuehe","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Sun Yat-sen University"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA250958","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:47:08Z","timestamp":1761126428000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA250958"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia250958","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}