{"type":"ScholarlyArticle","title":"Federated Adaptation for Foundation Model-based Recommendations","authors":[{"name":"Chunxu Zhang","slug":"chunxu-zhang","given":"Chunxu","family":"Zhang","affiliation":"Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education , China; College of Computer Science and Technology , Jilin University , China"},{"name":"Guodong Long","slug":"guodong-long","given":"Guodong","family":"Long","affiliation":"Australian Artificial Intelligence Institute , FEIT , University of Technology Sydney"},{"name":"Hongkuan Guo","slug":"hongkuan-guo","given":"Hongkuan","family":"Guo","affiliation":"Kuaishou Technology"},{"name":"Xiao Fang","slug":"xiao-fang","given":"Xiao","family":"Fang","affiliation":"Kuaishou Technology"},{"name":"Yang Song","slug":"yang-song","given":"Yang","family":"Song","affiliation":"Kuaishou Technology"},{"name":"Zhaojie Liu","slug":"zhaojie-liu","given":"Zhaojie","family":"Liu","affiliation":"Kuaishou Technology"},{"name":"Guorui Zhou","slug":"guorui-zhou","given":"Guorui","family":"Zhou","affiliation":"Kuaishou Technology"},{"name":"Zijian Zhang","slug":"zijian-zhang","given":"Zijian","family":"Zhang","affiliation":"Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education , China; College of Computer Science and Technology , Jilin University , China"},{"name":"Yang Liu","slug":"yang-liu","given":"Yang","family":"Liu","affiliation":"Institute for AI Industry Research , Tsinghua University"},{"name":"Bo Yang","slug":"bo-yang","given":"Bo","family":"Yang","affiliation":"Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education , China; College of Computer Science and Technology , Jilin University , China"}],"abstract":"With the recent success of large language models, particularly foundation models with generalization abilities, applying foundation models for recommendations becomes a new paradigm to improve existing recommendation systems. It becomes a new open challenge to enable the foundation model to capture user preference changes in a timely manner with reasonable communication and computation costs while preserving privacy. This paper proposes a novel federated adaptation mechanism to enhance the foundation model-based recommendation system in a privacy-preserving manner. Specifically, each client will learn a lightweight personalized adapter using its private data. The adapter then collaborates with pre-trained foundation models to provide recommendation service efficiently with fine-grained manners. Importantly, users' private behavioral data remains secure as it is not shared with the server. This data localization-based privacy preservation is embodied via the federated learning framework. The model can ensure that shared knowledge is incorporated into all adapters while simultaneously preserving each user's personal preferences. Experimental results on four benchmark datasets demonstrate our method's superior performance. The code is available.","doi":"10.24963/ijcai.2024/603","url":"https://ijcai.org/proceedings/2024/603","pdf":"https://ijcai.org/proceedings/2024/0603.pdf","markdown":"https://ijcai.org/proceedings/2024/603.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/603","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Machine Learning","track":"Main Track","number":603,"pages":{"first":5453,"last":5461},"keywords":["Data Mining: DM: Recommender systems","Data Mining: DM: Privacy-preserving data mining","Machine Learning: ML: Federated learning"],"videos":[],"references":[{"position":1,"raw":"[Achiam et al., 2023] Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Alt- man, Shyamal Anadkat, et al. 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