{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T18:25:12Z","timestamp":1775327112500,"version":"3.50.1"},"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>Secure aggregation is a common technique in federated learning (FL) for protecting data privacy from both external adversaries (eavesdroppers) and internal curious entities (clients or server). However, in dynamic and resource-constrained environments such as low Earth orbit (LEO) satellite networks, traditional secure aggregation methods fall short in two aspects: (1) the assumption on continuous client availability breaks due to intermittent and irregular visibility of the LEO satellites; (2) privacy leakage becomes possible over multiple communication rounds despite being protected in each single round. This paper proposes LTP-FLEO, an asynchronous FL framework that preserves long-term privacy (LTP) for LEO satellite networks. LTP-FLEO introduces (i) privacy-aware satellite partitioning, which groups satellites based on their predictable visibility to the server and enforces joint participation; (ii) model age balancing, which mitigates the adverse impact of stale model updates; and (iii) fair global aggregation, which treats satellites with different visibility durations in an equitable manner. Theoretical analysis and empirical validation demonstrate that LTP-FLEO effectively safeguards both model and data privacy across multi-round training, promotes fairness in line with satellite contributions, accelerates global convergence, and achieves competitive model accuracy.<\/jats:p>","DOI":"10.3233\/faia250920","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:46:04Z","timestamp":1761126364000},"source":"Crossref","is-referenced-by-count":1,"title":["When Secure Aggregation Falls Short: Achieving Long-Term Privacy in Asynchronous Federated Learning for LEO Satellite Networks"],"prefix":"10.3233","author":[{"given":"Mohamed","family":"Elmahallawy","sequence":"first","affiliation":[{"name":"Washington State University, mohamed.elmahallawy@wsu.edu"}]},{"given":"Tie","family":"Luo","sequence":"additional","affiliation":[{"name":"University of Kentucky, t.luo@uky.edu"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA250920","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:46:05Z","timestamp":1761126365000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA250920"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia250920","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]]}}}