Jun 18, 2022 · We propose Motley, a benchmark for personalized federated learning. Motley consists of a suite of cross-device and cross-silo federated datasets from varied ...
Personalized federated learning considers learning models unique to each client in a heterogeneous network. The resulting client-specific models have been ...
Oct 20, 2022 · We propose Motley, a benchmark for personalized federated learning. Motley consists of a suite of cross-device and cross-silo federated datasets from varied ...
Sep 26, 2022 · Motley aims to provide a reproducible means with which to advance developments in personalized and heterogeneity-aware federated learning, as ...
This directory contains code to reproduce the cross-silo experiments in our paper: Motley: Benchmarking Heterogeneity and Personalization in Federated Learning
Motley: Benchmarking Heterogeneity and Personalization in Federated Learning; Daniel Lopes, João Nadkarni, Filipe Assunção, Miguel Lopes and Luís Rodrigues ...
Data heterogeneity is one of the most challenging issues in federated learning, which motivates a variety of approaches to learn personalized models for ...
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Explore all code implementations available for Motley: Benchmarking Heterogeneity and Personalization in Federated Learning.
Motley: Benchmarking Heterogeneity and Personalization in Federated Learning · Shan-shan WuTian Li +4 authors. Virginia Smith. Computer Science. arXiv.org. 2022.
To better answer these questions, we propose Motley, a benchmark for personalized federated learning. Motley consists of a suite of cross-device and cross-silo ...