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Federated Evaluation and Tuning for On-Device Personalization: System Design & Applications

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arxiv 2102.08503 v1 pith:6SY26GUG submitted 2021-02-16 cs.LG

Matthias Paulik , Matt Seigel , Henry Mason , Dominic Telaar , Joris Kluivers , Rogier van Dalen , Chi Wai Lau , Luke Carlson
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Filip Granqvist Chris Vandevelde Sudeep Agarwal Julien Freudiger Andrew Byde Abhishek Bhowmick Gaurav Kapoor Si Beaumont Áine Cahill Dominic Hughes Omid Javidbakht Fei Dong Rehan Rishi Stanley Hung
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keywords federatedsystemdesignon-devicepersonalizationspecifictuningbeen
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We describe the design of our federated task processing system. Originally, the system was created to support two specific federated tasks: evaluation and tuning of on-device ML systems, primarily for the purpose of personalizing these systems. In recent years, support for an additional federated task has been added: federated learning (FL) of deep neural networks. To our knowledge, only one other system has been described in literature that supports FL at scale. We include comparisons to that system to help discuss design decisions and attached trade-offs. Finally, we describe two specific large scale personalization use cases in detail to showcase the applicability of federated tuning to on-device personalization and to highlight application specific solutions.

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Cited by 3 Pith papers

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    cs.CR 2025-02 conditional novelty 6.0 of 10

    Many FL defenses are evaluated on overly easy datasets and attacks, and this paper demonstrates with case studies that those easy settings can make weak defenses look strong.

  3. What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness

    cs.LG 2025-06 conditional novelty 4.0 of 10

    Under bounded second-order heterogeneity, local updates are shown to achieve faster convergence than mini-batch SGD in several convex and non-convex regimes, with matching lower bounds.

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