{"type":"ScholarlyArticle","title":"Multi-Agent Visualization for Explaining Federated Learning","authors":[{"name":"Xiguang Wei","slug":"xiguang-wei","given":"Xiguang","family":"Wei","affiliation":""},{"name":"Quan Li","slug":"quan-li","given":"Quan","family":"Li","affiliation":""},{"name":"Yang Liu","slug":"yang-liu","given":"Yang","family":"Liu","affiliation":"The Joint NTU-WeBank Research Centre of Eco-Intelligent Applications (THEIA)"},{"name":"Han Yu","slug":"han-yu","given":"Han","family":"Yu","affiliation":"School of Computer Science and Engineering , Nanyang Technological University (NTU)"},{"name":"Tianjian Chen","slug":"tianjian-chen","given":"Tianjian","family":"Chen","affiliation":""},{"name":"Qiang Yang","slug":"qiang-yang","given":"Qiang","family":"Yang","affiliation":"Department of Computer Science and Engineering , Hong Kong University of Science and Technology"}],"abstract":"As an alternative decentralized training approach, Federated Learning enables distributed agents to collaboratively learn a machine learning model while keeping personal/private information on local devices. However, one significant issue of this framework is the lack of transparency, thus obscuring understanding of the working mechanism of Federated Learning systems. This paper proposes a multi-agent visualization system that illustrates what is Federated Learning and how it supports multi-agents coordination. To be specific, it allows users to participate in the Federated Learning empowered multi-agent coordination. The input and output of Federated Learning are visualized simultaneously, which provides an intuitive explanation of Federated Learning for users in order to help them gain deeper understanding of the technology.","doi":"10.24963/ijcai.2019/960","url":"https://ijcai.org/proceedings/2019/960","pdf":"https://ijcai.org/proceedings/2019/0960.pdf","markdown":"https://ijcai.org/proceedings/2019/960.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/960","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Demos","track":"Demos","number":960,"pages":{"first":6572,"last":6574},"keywords":["AI: Multiagent Systems","AI: Human-Computer Interactive Systems","Applications: Education and training","AI: AI Modelling and Simulation"],"videos":[],"references":[{"position":1,"raw":"[Bonawitz et al., 2019] Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Konecny, Stefano Mazzocchi, H Brendan McMahan, et al. Towards fed- erated learning at scale: System design. arXiv preprint arXiv:1902.01046, 2019.","title":"Towards federated learning at scale: System design","authors":"Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Konecny, Stefano Mazzocchi, H Brendan Mcmahan","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Du et al., 2018] Mengnan Du, Ninghao Liu, and Xia Hu. Techniques for interpretable machine learning. arXiv preprint arXiv:1808.00033, 2018.","title":"Techniques for interpretable machine learning","authors":"Du","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Hankz Hankui Zhuo and Lin, 2019] Qian Xu Qiang Yang Hankz Hankui Zhuo, Wenfeng Feng and Yufeng Lin. Federated reinforcement learning. arXiv preprint arXiv:1901.08277, 2019.","title":"Federated reinforcement learning","authors":"Qian Xu, Qiang Yang, Hankz Hankui Zhuo, Wenfeng Feng, Yufeng Lin","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Huang and Liu, 2019] Li Huang and Dianbo Liu. Patient clustering improves efficiency of federated machine learn- ing to predict mortality and hospital stay time using distributed electronic medical records. arXiv preprint arXiv:1903.09296, 2019.","title":"Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records","authors":"Liu ; ] Li Huang, Dianbo Huang, Liu","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Huang et al., 2018] Li Huang, Yifeng Yin, Zeng Fu, Shifa Zhang, Hao Deng, and Dianbo Liu. Loadaboost: Loss- based adaboost federated machine learning on medical data. arXiv preprint arXiv:1811.12629, 2018.","title":"Loadaboost: Lossbased adaboost federated machine learning on medical data","authors":"Li Huang, Yifeng Yin, Zeng Fu, Shifa Zhang, Hao Deng, Dianbo Liu","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Konečnỳ et al., 2015] Jakub Konečnỳ, Brendan McMahan, and Daniel Ramage. Federated optimization: Distributed optimization beyond the datacenter. arXiv preprint arXiv:1511.03575, 2015.","title":"Federated optimization: Distributed optimization beyond the datacenter","authors":"Jakub Konečnỳ, Brendan Mcmahan, Daniel Ramage","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Konečnỳ et al., 2016] Jakub Konečnỳ, H Brendan McMa- han, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon. Federated learning: Strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492, 2016.","title":"Federated learning: Strategies for improving communication efficiency","authors":"Jakub Konečnỳ, Brendan Mcmahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, Dave Bacon","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Kumar et al., 2017] Saurabh Kumar, Pararth Shah, Dilek Hakkani-Tur, and Larry Heck. Federated control with hi- erarchical multi-agent deep reinforcement learning. arXiv preprint arXiv:1712.08266, 2017.","title":"Federated control with hierarchical multi-agent deep reinforcement learning","authors":"Saurabh Kumar, Pararth Shah, Dilek Hakkani-Tur, Larry Heck","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Liu et al., 2018] Yang Liu, Tianjian Chen, and Qiang Yang. Secure federated transfer learning. arXiv preprint arXiv:1812.03337, 2018.","title":"Secure federated transfer learning","authors":"Yang Liu, Tianjian Chen, Qiang Yang","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[McMahan and Ramage, 2017] Brendan McMahan and Daniel Ramage. Federated learning: Collaborative ma- chine learning without centralized training data. Google Research Blog, 3, 2017.","title":"Federated learning: Collaborative machine learning without centralized training data","authors":"Ramage Mcmahan, Brendan Mcmahan, Daniel Ramage","year":"2017","venue":"Google Research Blog","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Mike, 2018] Mike. Federated learning: distributed machine learning with data locality and privacy. https://blog. fastforwardlabs.com/2018/11/14/federated-learning.html, 2018.","title":"Federated learning: distributed machine learning with data locality and privacy","authors":"Mike","year":"2018","venue":null,"doi":null,"url":"https://blog.fastforwardlabs.com/2018/11/14/federated-learning.html","internal_url":null},{"position":12,"raw":"[Regulation, 2016] Protection Regulation. Regulation (eu) 2016/679 of the european parliament and of the council. REGULATION (EU), 679, 2016.","title":"Protection Regulation. Regulation (eu) 2016/679 of the european parliament and of the council. REGULATION (EU)","authors":null,"year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Rojek, 2018] Marcin Rojek. Devices learning from each other? see it live this september at ai summit in san francisco! https://medium.com/@marcrojek/ devices-learning-from-each-other-see-it-live-this\\ -september-at-ai-summit-in-san-francisco-9ac6462d7f8e, 2018.","title":"Devices learning from each other? see it live this september at ai summit in san francisco!","authors":"Marcin Rojek","year":"2018","venue":null,"doi":null,"url":"https://medium.com/@marcrojek/devices-learning-from-each-other-see-it-live-this\\-september-at-ai-summit-in-san-francisco-9ac6462d7f8","internal_url":null},{"position":14,"raw":"[Voigt and Von dem Bussche, 2017] Paul Voigt and Axel Von dem Bussche. The eu general data protection regu- lation (gdpr). A Practical Guide, 1st Ed., Cham: Springer International Publishing, 2017.","title":"The eu general data protection regulation (gdpr). A Practical Guide","authors":"Von Voigt, Dem Bussche","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Yang et al., 2019] Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong. Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST), 10(2):12, 2019.","title":"Federated machine learning: Concept and applications","authors":"Qiang Yang, Yang Liu, Tianjian Chen, Yongxin Tong","year":"2019","venue":"ACM Transactions on Intelligent Systems and Technology (TIST)","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}