{"type":"ScholarlyArticle","title":"Intelligent Agents for Auction-based Federated Learning: A Survey","authors":[{"name":"Xiaoli Tang","slug":"xiaoli-tang","given":"Xiaoli","family":"Tang","affiliation":"College of Computing and Data Science , Nanyang Technological University , Singapore"},{"name":"Han Yu","slug":"han-yu","given":"Han","family":"Yu","affiliation":"College of Computing and Data Science , Nanyang Technological University , Singapore"},{"name":"Xiaoxiao Li","slug":"xiaoxiao-li","given":"Xiaoxiao","family":"Li","affiliation":"Department of Electrical and Computer Engineering , The University of British Columbia , Canada"},{"name":"Sarit Kraus","slug":"sarit-kraus","given":"Sarit","family":"Kraus","affiliation":"College of Computing and Data Science , Nanyang Technological University , Singapore; Department of Computer Science , Bar-Ilan University , Israel"}],"abstract":"Auction-based federated learning (AFL) is an important emerging category of FL incentive mechanism design, due to its ability to fairly and efficiently motivate high-quality data owners to join data consumers' (i.e., servers') FL training tasks. To enhance the efficiency in AFL decision support for stakeholders (i.e., data consumers, data owners, and the auctioneer), intelligent agent-based techniques have emerged. However, due to the highly interdisciplinary nature of this field and the lack of a comprehensive survey providing an accessible perspective, it is a challenge for researchers to enter and contribute to this field. This paper bridges this important gap by providing a first-of-its-kind survey on the Intelligent Agents for AFL (IA-AFL) literature. We propose a unique multi-tiered taxonomy that organises existing IA-AFL works according to 1) the stakeholders served, 2) the auction mechanism adopted, and 3) the goals of the agents, to provide readers with a multi-perspective view into this field. In addition, we analyse the limitations of existing approaches, summarise the commonly adopted performance evaluation metrics, and discuss promising future directions leading towards effective and efficient stakeholder-oriented decision support in IA-AFL ecosystems.","doi":"10.24963/ijcai.2024/912","url":"https://ijcai.org/proceedings/2024/912","pdf":"https://ijcai.org/proceedings/2024/0912.pdf","markdown":"https://ijcai.org/proceedings/2024/912.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/912","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":912,"pages":{"first":8253,"last":8261},"keywords":["Machine Learning: ML: Federated learning"],"videos":[],"references":[{"position":1,"raw":"[Ali et al., 2021] Asad Ali, Inaam Ilahi, Adnan Qayyum, Ihab Mo- hammed, Ala Al-Fuqaha, and Junaid Qadir. 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