{"type":"ScholarlyArticle","title":"A Systematic Survey on Federated Semi-supervised Learning","authors":[{"name":"Zixing Song","slug":"zixing-song","given":"Zixing","family":"Song","affiliation":"The Chinese University of Hong Kong , Hong Kong SAR , China"},{"name":"Xiangli Yang","slug":"xiangli-yang","given":"Xiangli","family":"Yang","affiliation":"University of Electronic Science and Technology of China , Chengdu , China"},{"name":"Yifei Zhang","slug":"yifei-zhang","given":"Yifei","family":"Zhang","affiliation":"The Chinese University of Hong Kong , Hong Kong SAR , China"},{"name":"Xinyu Fu","slug":"xinyu-fu","given":"Xinyu","family":"Fu","affiliation":"The Chinese University of Hong Kong , Hong Kong SAR , China"},{"name":"Zenglin Xu","slug":"zenglin-xu","given":"Zenglin","family":"Xu","affiliation":"Harbin Institute of Technology , Shenzhen , China; Peng Cheng Laboratory , Shenzhen , China"},{"name":"Irwin King","slug":"irwin-king","given":"Irwin","family":"King","affiliation":"The Chinese University of Hong Kong , Hong Kong SAR , China"}],"abstract":"Federated learning (FL) revolutionizes distributed machine learning by enabling devices to collaboratively learn a model while maintaining data privacy. However, FL usually faces a critical challenge with limited labeled data, making semi-supervised learning (SSL) crucial for utilizing abundant unlabeled data. The integration of SSL within the federated framework gives rise to federated semi-supervised learning (FSSL), a novel approach that exploits unlabeled data across devices without compromising privacy. This paper systematically explores FSSL, shedding light on its four basic problem settings that commonly appear in real-world scenarios. By examining the unique challenges, generic solutions, and representative methods tailored for each setting of FSSL, we aim to provide a cohesive overview of the current state of the art and pave the way for future research directions in this promising field.","doi":"10.24963/ijcai.2024/911","url":"https://ijcai.org/proceedings/2024/911","pdf":"https://ijcai.org/proceedings/2024/0911.pdf","markdown":"https://ijcai.org/proceedings/2024/911.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/911","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":"Survey Track","track":"Survey Track","number":911,"pages":{"first":8244,"last":8252},"keywords":["Machine Learning: ML: Federated learning","Machine Learning: ML: Semi-supervised learning","Machine Learning: General"],"videos":[],"references":[{"position":1,"raw":"[Che et al., 2021] Liwei Che, Zewei Long, Jiaqi Wang, Yaqing Wang, Houping Xiao, and Fenglong Ma. 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