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arXiv:1912.04977cs.LG

Advances and Open Problems in Federated Learning

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

Early discovery. Linked by one identifiable commenter. This paper is outside the main ranking; a mention is not an endorsement.

Abstract

Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g. service provider), while keeping the training data decentralized. 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.

arXiv comments
Published in Foundations and Trends in Machine Learning Vol 4 Issue 1. See: https://www.nowpublishers.com/article/Details/MAL-083
Journal reference
DOI
Subjects
cs.LGMachine Learning cs.CRCryptography and Security stat.MLMachine Learning (Statistics)

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Linked by 1 identifiable commenter in 3 comments across 3 threads, from Dec 2020 to Aug 2023.

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Discussed in comments3 threads

  1. Open Challenges in LLM Research 1 comment

    • I really like seeing articles or papers that describe the current advances and open challenges in a sub-field (such as [0]). They're underappreciated, but good practice or reading for folks wanting to get in the field. They're also worthwhile and humbling to look back at every…

      techwizrdView on HNParent context

  2. Run 100B+ language models at home, BitTorrent‑style 1 comment

    • I'm not entirely how the approach they're using works [0], but I study federated learning and one of the highly-cited survey papers has several chapters (5 and 6 in particular) addressing potential attacks, failure modes, and bias [1]. 0…

      techwizrdView on HNParent context

  3. Flower – A Friendly Federated Learning Framework 1 comment

    • For those interested or confused around federated learning, I suggest skimming the 2019 paper "Advances and Open Problems in Federated Learning" [0]. The introduction is quite good and the table of contents gives a good overview of the current challenges in the field. From the…

      techwizrdView on HNParent context

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