Advances and Open Problems in Federated Learning

12/10/2019

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by Peter Kairouz, et al.

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Google

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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.

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Related Research

Better Methods and Theory for Federated Learning: Compression, Client Selection and Heterogeneity

Correct orchestration of Federated Learning generic algorithms: formalisation and verification in CSP

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Prospects of federated machine learning in fluid dynamics

How Much Does It Cost to Train a Machine Learning Model over Distributed Data Sources?

Conformal Prediction for Federated Uncertainty Quantification Under Label Shift

Mixed Federated Learning: Joint Decentralized and Centralized Learning

Code Repositories

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Resources_for_private_and_secure_AI