{"type":"ScholarlyArticle","title":"BARA: Efficient Incentive Mechanism with Online Reward Budget Allocation in Cross-Silo Federated Learning","authors":[{"name":"Yunchao Yang","slug":"yunchao-yang","given":"Yunchao","family":"Yang","affiliation":"School of Computer Science and Engineering , Sun Yat-sen University , Guangzhou , China; Guangdong Key Laboratory of Big Data Analysis and Processing , Guangzhou , China"},{"name":"Yipeng Zhou","slug":"yipeng-zhou","given":"Yipeng","family":"Zhou","affiliation":"School of Computing , Faculty of Science and Engineering , Macquarie University , Sydney , Australia"},{"name":"Miao Hu","slug":"miao-hu","given":"Miao","family":"Hu","affiliation":"School of Computer Science and Engineering , Sun Yat-sen University , Guangzhou , China; Guangdong Key Laboratory of Big Data Analysis and Processing , Guangzhou , China"},{"name":"Di Wu","slug":"di-wu","given":"Di","family":"Wu","affiliation":"School of Computer Science and Engineering , Sun Yat-sen University , Guangzhou , China; Guangdong Key Laboratory of Big Data Analysis and Processing , Guangzhou , China"},{"name":"Quan Z. Sheng","slug":"quan-z-sheng","given":"Quan Z.","family":"Sheng","affiliation":"School of Computing , Faculty of Science and Engineering , Macquarie University , Sydney , Australia"}],"abstract":"Federated learning (FL) is a prospective distributed machine learning  framework that can preserve data privacy. In particular, cross-silo FL can complete model training by making isolated data islands of different organizations collaborate with a parameter server (PS) via exchanging model parameters for multiple communication rounds. In cross-silo FL, an incentive mechanism is indispensable  for motivating data owners to contribute their models to FL training. However, how to allocate the reward budget among different rounds is an essential but complicated problem  largely overlooked by existing works. The challenge of this problem lies in the opaque feedback between reward budget allocation and model utility improvement of FL, making the optimal reward budget allocation complicated. To address this problem, we design an online reward budget allocation algorithm using Bayesian optimization named BARA (Budget Allocation for Reverse Auction). Specifically, BARA can model the complicated relationship between reward budget allocation and final model accuracy in FL based on historical training records so that the reward budget allocated to each communication round is dynamically optimized so as to maximize the final model utility. We further incorporate the BARA algorithm into reverse auction-based incentive mechanisms to illustrate its effectiveness. Extensive experiments are conducted on real datasets to demonstrate that BARA significantly outperforms competitive baselines by improving  model utility with the same amount of reward budget.","doi":"10.24963/ijcai.2023/498","url":"https://ijcai.org/proceedings/2023/498","pdf":"https://ijcai.org/proceedings/2023/0498.pdf","markdown":"https://ijcai.org/proceedings/2023/498.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/498","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Machine Learning","track":"Main Track","number":498,"pages":{"first":4478,"last":4485},"keywords":["Machine Learning: ML: Federated learning"],"videos":[],"references":[{"position":1,"raw":"[Bogunovic et al., 2016] Ilija Bogunovic, Jonathan Scarlett, and Volkan Cevher. 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