{"type":"ScholarlyArticle","title":"Towards Verifiable Federated Learning","authors":[{"name":"Yanci Zhang","slug":"yanci-zhang","given":"Yanci","family":"Zhang","affiliation":"School of Computer Science and Engineering , Nanyang Technological University , Singapore"},{"name":"Han Yu","slug":"han-yu","given":"Han","family":"Yu","affiliation":"School of Computer Science and Engineering , Nanyang Technological University , Singapore"}],"abstract":"Federated learning (FL) is an emerging paradigm of collaborative machine learning that preserves user privacy while building powerful models. Nevertheless, due to the nature of open participation by self-interested entities, it needs to guard against potential misbehaviours by legitimate FL participants. FL verification techniques are promising solutions for this problem. They have been shown to effectively enhance the reliability of FL networks and build trust among participants. Verifiable FL has become an emerging topic of research that has attracted significant interest from the academia and the industry alike. Currently, there is no comprehensive survey on the field of verifiable federated learning, which is interdisciplinary in nature and can be challenging for researchers to enter into. In this paper, we bridge this gap by reviewing works focusing on verifiable FL. We propose a novel taxonomy for verifiable FL covering both centralised and decentralised settings, summarise the commonly adopted performance evaluation approaches, and discuss promising directions towards a versatile verifiable FL framework.","doi":"10.24963/ijcai.2022/792","url":"https://ijcai.org/proceedings/2022/792","pdf":"https://ijcai.org/proceedings/2022/0792.pdf","markdown":"https://ijcai.org/proceedings/2022/792.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/792","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Survey Track","track":"Survey Track","number":792,"pages":{"first":5686,"last":5693},"keywords":["Survey Track: AI Ethics, Trust, Fairness","Survey Track: Machine Learning","Survey Track: Humans and AI","Survey Track: Multidisciplinary Topics and Applications"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38987289","length_sec":62,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985432","length_sec":546,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Bao et al., 2019] Xianglin Bao, Cheng Su, et al. 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