Papaya: Practical, Private, and Scalable Federated Learning

By Dzmitry Huba, John Nguyen, Kshitiz Malik, Ruiyu Zhu, Michael G. Rabbat, Ashkan Yousefpour, Carole-Jean Wu, Hongyuan Zhan, Pavel Ustinov, H. Srinivas and 4 more. 2021

172 citations recorded by the source corpus.

Cross-device Federated Learning (FL) is a distributed learning paradigm with several challenges that differentiate it from traditional distributed learning, variability in the system characteristics on each device, and millions of clients coordinating with a central server being primary ones. Most FL systems described in the literature are synchronous - they perform a synchronized aggregation of model updates from individual clients. Scaling synchronous FL is challenging since increasing the number of clients training in parallel leads to diminishing returns in training speed, analogous to large-batch training. Moreover, stragglers hinder synchronous FL training. In this work, we outline a production asynchronous FL system design. Our work tackles the aforementioned issues, sketches of some of the system design challenges and their solutions, and touches upon principles that emerged from building a production FL system for millions of clients. Empirically, we demonstrate that asynchronous FL converges faster than synchronous FL when training across nearly one hundred million devices. In particular, in high concurrency settings, asynchronous FL is 5x faster and has nearly 8x less communication overhead than synchronous FL.

Subfields: Artificial Intelligence, Safety, Robustness & Interpretability

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Citations in the corpus

Sustainable AI: Environmental Implications, Challenges and Opportunities (2021, background): “On-Device Learning On-device AI is becoming more ubiquitously adopted to enable model personalization [84], [85], [86] while improving data privacy [87], [88], [89], [90], yet its impact in terms of carbon emission is often overlooked.”