Pith. sign in

REVIEW 3 cited by

Federated Evaluation of On-device Personalization

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1910.10252 v1 pith:VGZLWB45 submitted 2019-10-22 cs.LG stat.ML

Kangkang Wang , Rajiv Mathews , Chloé Kiddon , Hubert Eichner , Françoise Beaufays , Daniel Ramage This is my paper
classification cs.LGstat.ML
keywords personalizationmodelsevaluatefederatedframeworkglobalon-deviceusers
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Federated learning is a distributed, on-device computation framework that enables training global models without exporting sensitive user data to servers. In this work, we describe methods to extend the federation framework to evaluate strategies for personalization of global models. We present tools to analyze the effects of personalization and evaluate conditions under which personalization yields desirable models. We report on our experiments personalizing a language model for a virtual keyboard for smartphones with a population of tens of millions of users. We show that a significant fraction of users benefit from personalization.

Discussion (0). Sign in to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. FedSPM: Routing-Enabled Federated Learning under Dual Heterogeneity via Semiparametric Mixture

    cs.LG 2026-07 conditional novelty 6.0 of 10

    A semiparametric mixture federated model with density-ratio routing and a federated EM algorithm improves routing and prediction under joint inter- and intra-client heterogeneity.

  2. Collate: Collaborative Neural Network Learning for Latency-Critical Edge Systems

    cs.LG 2026-07 conditional novelty 5.5 of 10

    Collate jointly trains heterogeneous models under per-device latency constraints via dynamic zeroizing-recovering and proto-corrected aggregation, gaining ~2–3% accuracy over prior heterogeneous FL.

  3. Generalizable Federated Learning using Client Adaptive Focal Modulation

    cs.CV 2025-08 reject novelty 4.0 of 10

    The abstract describes AdaptFED, a claimed federated learning method, but the full text is an unrelated graph theory paper, so the claimed results are absent from the submission.

Pith tools