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Inf. Syst."],"published-print":{"date-parts":[[2026,3,31]]},"abstract":"<jats:p>\n                    With growing concerns over user privacy, federated recommendation (FedRec) has emerged as a mainstream solution for personalized recommendation services. FedRec trains user-private parameters on local clients while collaboratively updating global parameters on a centralized server. However, despite advances in optimizing these local and global parameters, existing methods overlook two key challenges:\n                    <jats:italic toggle=\"yes\">tradeoff training<\/jats:italic>\n                    and\n                    <jats:italic toggle=\"yes\">distribution discrepancy<\/jats:italic>\n                    . Tradeoff training balances timely local updates with diverse global parameters, limiting the model\u2019s learning ability. Distribution discrepancy arises from the divergence between locally trained global parameters and those aggregated by the server, corrupting inference performance. To fill in the gap, we propose\n                    <jats:italic toggle=\"yes\">FedSC<\/jats:italic>\n                    , a principled federated recommendation framework that boosts FedRec\u2019s training and inference processes with minimal yet nontrivial efforts. During training, FedSC employs a stochastic aggregation strategy where all users participate in every round, while only a random subset is selected for aggregation, preserving the diversity of global parameters and ensuring timely local updates. During inference, FedSC makes recommendations with a consistency inference mechanism that uses the most recent locally trained global parameters of each user to improve the model\u2019s understanding of user preferences. Extensive experiments on multiple benchmark datasets demonstrate the superiority of FedSC, achieving up to a 20% improvement in most evaluation scenarios.\n                  <\/jats:p>","DOI":"10.1145\/3795526","type":"journal-article","created":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T14:11:49Z","timestamp":1770127909000},"page":"1-40","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Federated Recommendation via Stochastic Aggregation and Consistency Inference"],"prefix":"10.1145","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-9872-2798","authenticated-orcid":false,"given":"Xiaoqiang","family":"Gui","sequence":"first","affiliation":[{"name":"School of Software, Shandong University, Jinan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-1537-0936","authenticated-orcid":false,"given":"Bowen","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Software, Shandong University, Jinan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8999-968X","authenticated-orcid":false,"given":"Qiaoyu","family":"Tan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, New York University Shanghai, Pudong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5890-0365","authenticated-orcid":false,"given":"Jun","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Software, Shandong University, Jinan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5921-858X","authenticated-orcid":false,"given":"Yongqing","family":"Zheng","sequence":"additional","affiliation":[{"name":"Dareway Software Co., Ltd, Jinan, China and School of Software, Shandong University, Jinan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7626-3929","authenticated-orcid":false,"given":"Qingzhong","family":"Li","sequence":"additional","affiliation":[{"name":"School of Software, Shandong University, Jinan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8262-8883","authenticated-orcid":false,"given":"Lizhen","family":"Cui","sequence":"additional","affiliation":[{"name":"School of Software, Shandong University, Jinan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1667-6705","authenticated-orcid":false,"given":"Guoxian","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Software, Shandong University, Jinan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,3,25]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"Muhammad Ammad-Ud-Din Elena Ivannikova Suleiman A. 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