Advances and Open Problems in Federated Learning

Kairouz, Peter, McMahan, H. Brendan, Avent, Brendan, Bellet, Aurélien, Bennis, Mehdi, Bhagoji, Arjun Nitin, Bonawitz, Keith, Charles, Zachary, Cormode, Graham, Cummings, Rachel, D'Oliveira, Rafael G. L., Rouayheb, Salim El, Evans, David, Gardner, Josh, Garrett, Zachary, Gascón, Adrià, Ghazi, Badih, Gibbons, Phillip B., Gruteser, Marco, Harchaoui, Zaid, He, Chaoyang, He, Lie, Huo, Zhouyuan, Hutchinson, Ben, Hsu, Justin, Jaggi, Martin, Javidi, Tara, Joshi, Gauri, Khodak, Mikhail, Konečný, Jakub, Korolova, Aleksandra, Koushanfar, Farinaz, Koyejo, Sanmi, Lepoint, Tancrède, Liu, Yang, Mittal, Prateek, Mohri, Mehryar, Nock, Richard, Özgür, Ayfer, Pagh, Rasmus, Raykova, Mariana, Qi, Hang, Ramage, Daniel, Raskar, Ramesh, Song, Dawn, Song, Weikang, Stich, Sebastian U., Sun, Ziteng, Suresh, Ananda Theertha, Tramèr, Florian, Vepakomma, Praneeth, Wang, Jianyu, Xiong, Li, Xu, Zheng, Yang, Qiang, Yu, Felix X., Yu, Han, Zhao, Sen

–arXiv.org Machine Learning 

FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. Motivated by the explosive growth in FL research, this paper discusses recent advances and presents an extensive collection of open problems and challenges. Peter Kairouz and H. Brendan McMahan conceived, coordinated, and edited this work.

arXiv.org Machine Learning

Dec-10-2019

arXiv.org    PDF

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