{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T15:41:58Z","timestamp":1783525318123,"version":"3.55.0"},"reference-count":75,"publisher":"Association for Computing Machinery (ACM)","issue":"6","funder":[{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"crossref","award":["FT210100624, DP240101108, LP230200892"],"award-info":[{"award-number":["FT210100624, DP240101108, LP230200892"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2025,11,30]]},"abstract":"<jats:p>Sequential recommendation has been widely studied in the recommendation domain since it can capture users\u2019 temporal preferences and provide more accurate and timely recommendations. To address user privacy concerns, the combination of federated learning and sequential recommender systems (FedSeqRec) has gained growing attention. Unfortunately, the performance of FedSeqRec is still unsatisfactory because the models used in FedSeqRec have to be lightweight to accommodate communication bandwidth and clients\u2019 on-device computational resource constraints. Recently, large language models (LLMs) have exhibited strong transferable and generalized language understanding abilities and therefore, in the NLP area, many downstream tasks now utilize LLMs as a service to achieve superior performance without constructing complex models. Inspired by this successful practice, we propose a generic FedSeqRec framework, FELLAS, which aims to enhance FedSeqRec by utilizing LLMs as an external service.<\/jats:p>\n          <jats:p>\n            Specifically, FELLAS employs an LLM server to provide both item-level and sequence-level representation assistance. The item-level representation service is queried by the central server to enrich the original ID-based item embedding with textual information, while the sequence-level representation service is accessed by each client. However, invoking the sequence-level representation service requires clients to send sequences to the external LLM server. To safeguard privacy, we implement\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(d_{\\mathcal{X}}\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            -privacy satisfied sequence perturbation, which protects clients\u2019 sensitive data with guarantees. Additionally, a contrastive learning-based method is designed to transfer knowledge from the noisy sequence representation to clients\u2019 sequential recommendation models. Furthermore, to empirically validate the privacy protection capability of FELLAS, we propose two interacted item inference attacks, considering the threats posed by the LLM server and the central server acting as curious-but-honest adversaries in cooperation. Extensive experiments conducted on three datasets with two widely used sequential recommendation models demonstrate the effectiveness and privacy-preserving capability of FELLAS.\n          <\/jats:p>","DOI":"10.1145\/3709138","type":"journal-article","created":{"date-parts":[[2024,12,21]],"date-time":"2024-12-21T00:09:26Z","timestamp":1734739766000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":23,"title":["FELLAS: Enhancing Federated Sequential Recommendation with LLM as External Services"],"prefix":"10.1145","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9400-842X","authenticated-orcid":false,"given":"Wei","family":"Yuan","sequence":"first","affiliation":[{"name":"The University of Queensland - St Lucia Campus, Brisbane, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6756-3068","authenticated-orcid":false,"given":"Chaoqun","family":"Yang","sequence":"additional","affiliation":[{"name":"Griffith University, Nathan, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1683-1875","authenticated-orcid":false,"given":"Guanhua","family":"Ye","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7269-146X","authenticated-orcid":false,"given":"Tong","family":"Chen","sequence":"additional","affiliation":[{"name":"The University of Queensland - St Lucia Campus, Brisbane, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9687-1315","authenticated-orcid":false,"given":"Quoc Viet Hung","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Griffith University - Gold Coast Campus, Southport, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1395-261X","authenticated-orcid":false,"given":"Hongzhi","family":"Yin","sequence":"additional","affiliation":[{"name":"School of EECS, The University of Queensland, Saint Lucia, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,9,12]]},"reference":[{"key":"e_1_3_2_2_1","unstructured":"Muhammad Ammad-Ud-Din Elena Ivannikova Suleiman A. 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