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(Track1) Federated Learning and Analytics: Industry Meets Academia
Brendan McMahan · Virginia Smith · Peter Kairouz

Mon Dec 07 01:30 PM -- 04:00 PM (PST) @ None

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. Similarly, federated analytics (FA) allows data scientists to generate analytical insight from the combined information in distributed datasets without requiring data centralization. Federated approaches embody 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 federated learning and analytics research, this tutorial will provide a gentle introduction to the area. The focus will be on cross-device federated learning, including deep dives on federated optimization and differentially privacy, but federated analytics and cross-silo federated learning will also be discussed. In addition to optimization and privacy, we will also introduce personalization, robustness, fairness, and systems challenges in the federated setting with an emphasis on open problems.

Author Information

Brendan McMahan (Google)
Virginia Smith (Carnegie Mellon University)
Peter Kairouz (Google)

Peter Kairouz is a Google Research Scientist working on decentralized, privacy-preserving, and robust machine learning algorithms. Prior to Google, his research largely focused on building decentralized technologies for anonymous broadcasting over complex networks, understanding the fundamental trade-off between differential privacy and utility of learning algorithms, and leveraging state-of-the-art deep generative models for data-driven privacy and fairness.

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