{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T17:52:58Z","timestamp":1772905978241,"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>Cross-client data heterogeneity in federated learning induces biases that impede unbiased consensus condensation and the complementary fusion of generalization- and personalization-oriented knowledge. While existing approaches mitigate heterogeneity through model decoupling and representation center loss, they often rely on static and restricted metrics to evaluate local knowledge and adopt global alignment too rigidly, leading to consensus distortion and diminished model adaptability. To address these limitations, we propose FedMate(Code: https:\/\/github.com\/Dongrun-Li\/FedMate.git. Full version of this paper can be found in [39].), a method that implements bilateral optimization: On the server side, we construct a dynamic global prototype, with aggregation weights calibrated by holistic integration of sample size, current parameters, and future prediction; a category-wise classifier is then fine-tuned using this prototype to preserve global consistency. On the client side, we introduce complementary classification fusion to enable merit-based discrimination training and incorporate cost-aware feature transmission to balance model performance and communication efficiency. Experiments on five datasets of varying complexity demonstrate that FedMate outperforms state-of-the-art methods in harmonizing generalization and adaptation. Additionally, semantic segmentation experiments on autonomous driving datasets validate the method\u2019s real-world scalability.<\/jats:p>","DOI":"10.3233\/faia251150","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:52:56Z","timestamp":1761126776000},"source":"Crossref","is-referenced-by-count":1,"title":["Choice Outweighs Effort: Facilitating Complementary Knowledge Fusion in Federated Learning via Re-Calibration and Merit-Discrimination"],"prefix":"10.3233","author":[{"given":"Ming","family":"Yang","sequence":"first","affiliation":[{"name":"Key Lab. of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China"},{"name":"Shandong Provincial Key Lab. of Industrial Network and Information System Security, Shandong Fundamental Research Center for Computer Science, Jinan, China"}]},{"given":"Dongrun","family":"Li","sequence":"additional","affiliation":[{"name":"Key Lab. of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China"},{"name":"Shandong Provincial Key Lab. of Industrial Network and Information System Security, Shandong Fundamental Research Center for Computer Science, Jinan, China"}]},{"given":"Xin","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Lab. of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China"},{"name":"Shandong Provincial Key Lab. of Industrial Network and Information System Security, Shandong Fundamental Research Center for Computer Science, Jinan, China"}]},{"given":"Xiaoyang","family":"Yu","sequence":"additional","affiliation":[{"name":"Key Lab. of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China"},{"name":"Shandong Provincial Key Lab. of Industrial Network and Information System Security, Shandong Fundamental Research Center for Computer Science, Jinan, China"}]},{"given":"Xiaoming","family":"Wu","sequence":"additional","affiliation":[{"name":"Key Lab. of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China"},{"name":"Shandong Provincial Key Lab. of Industrial Network and Information System Security, Shandong Fundamental Research Center for Computer Science, Jinan, China"}]},{"given":"Shibo","family":"He","sequence":"additional","affiliation":[{"name":"College of Control Science and Engineering, Zhejiang University, Hangzhou, China"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251150","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:52:57Z","timestamp":1761126777000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251150"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251150","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]]}}}