Advances and Open Problems in Federated Learning

Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, and others


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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.


BibTeX
@misc{KMAB+19,
  author   =   {Kairouz, Peter and McMahan, H Brendan and Avent, Brendan and Bellet, Aur{\'e}lien and Bennis, Mehdi and Bhagoji, Arjun Nitin and Bonawitz, Keith and Charles, Zachary and Cormode, Graham and Cummings, Rachel and others},
  title   =   {Advances and Open Problems in Federated Learning},
  year   =   {2019},
  howpublished   =   {arXiv preprint arXiv:1912.04977},
  url   =   {https://arxiv.org/abs/1912.04977}
}