{"type":"ScholarlyArticle","title":"FedPFT: Federated Proxy Fine-Tuning of Foundation Models","authors":[{"name":"Zhaopeng Peng","slug":"zhaopeng-peng","given":"Zhaopeng","family":"Peng","affiliation":"Fujian Key Laboratory of Sensing and Computing for Smart Cities , School of Informatics , Xiamen University"},{"name":"Xiaoliang Fan","slug":"xiaoliang-fan","given":"Xiaoliang","family":"Fan","affiliation":"Fujian Key Laboratory of Sensing and Computing for Smart Cities , School of Informatics , Xiamen University"},{"name":"Yufan Chen","slug":"yufan-chen","given":"Yufan","family":"Chen","affiliation":"Fujian Key Laboratory of Sensing and Computing for Smart Cities , School of Informatics , Xiamen University"},{"name":"Zheng Wang","slug":"zheng-wang","given":"Zheng","family":"Wang","affiliation":"Fujian Key Laboratory of Sensing and Computing for Smart Cities , School of Informatics , Xiamen University"},{"name":"Shirui Pan","slug":"shirui-pan","given":"Shirui","family":"Pan","affiliation":"School of Information and Communication Technology , Griffith University"},{"name":"Chenglu Wen","slug":"chenglu-wen","given":"Chenglu","family":"Wen","affiliation":"Fujian Key Laboratory of Sensing and Computing for Smart Cities , School of Informatics , Xiamen University"},{"name":"Ruisheng Zhang","slug":"ruisheng-zhang","given":"Ruisheng","family":"Zhang","affiliation":"School of Information Science and Engineering , Lanzhou University"},{"name":"Cheng Wang","slug":"cheng-wang","given":"Cheng","family":"Wang","affiliation":"Fujian Key Laboratory of Sensing and Computing for Smart Cities , School of Informatics , Xiamen University"}],"abstract":"Adapting Foundation Models (FMs) for down- stream tasks through Federated Learning (FL) emerges a promising strategy for protecting data privacy and valuable FMs. Existing methods fine- tune FM by allocating sub-FM to clients in FL, however, leading to suboptimal performance due to insufficient tuning and inevitable error accumula- tions of gradients. In this paper, we propose Feder- ated Proxy Fine-Tuning (FedPFT), a novel method enhancing FMs adaptation in downstream tasks through FL by two key modules. First, the sub-FM construction module employs a layer-wise com- pression approach, facilitating comprehensive FM fine-tuning across all layers by emphasizing those crucial neurons. Second, the sub-FM alignment module conducts a two-step distillations—layer- level and neuron-level—before and during FL fine- tuning respectively, to reduce error of gradient by accurately aligning sub-FM with FM under theo- retical guarantees. Experimental results on seven commonly used datasets (i.e., four text and three vi- sion) demonstrate the superiority of FedPFT.  Our code is available at https://github.com/pzp-dzd/FedPFT.","doi":"10.24963/ijcai.2024/531","url":"https://ijcai.org/proceedings/2024/531","pdf":"https://ijcai.org/proceedings/2024/0531.pdf","markdown":"https://ijcai.org/proceedings/2024/531.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/531","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":531,"pages":{"first":4806,"last":4814},"keywords":["Machine Learning: ML: Trustworthy machine learning","Multidisciplinary Topics and Applications: MTA: Security and privacy","Machine Learning: ML: Federated learning"],"videos":[],"references":[{"position":1,"raw":"[Bommasani et al., 2021] Rishi Bommasani, Drew A Hud- son, Ehsan Adeli, Russ Altman, Simran Arora, Syd- ney von Arx, Michael S Bernstein, Jeannette Bohg, An- toine Bosselut, Emma Brunskill, et al. 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