Oct 1, 2019 · Such datasets pose a significant challenge to decentralized learning because their different contexts result in significant data distribution ...
Our study shows that: (i) skewed data labels are a fundamental and pervasive problem for decentralized learning, causing signifi- cant accuracy loss across many ...
Many large-scale machine learning (ML) applications need to perform decentralized learning over datasets generated at different devices and locations.
Our study shows that: (i) skewed data labels are a fundamental and pervasive problem for decentralized learning, causing signifi- cant accuracy loss across many ...
The Non-IID Data Quagmire of Decentralized Machine Learning. Proceedings of the 37th International Conference on Machine Learning (ICML), 2020. Kevin Hsieh ...
The Non-IID Data Quagmire of. Decentralized Machine Learning. Kevin Hsieh, Amar Phanishayee, Onur Mutlu, Phillip Gibbons. ICML 2020. Page 2. ML Training with ...
Sep 7, 2024 · These decentralized datasets pose a fundamental challenge to ML because they are typically generated in very different contexts, which leads to ...
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Our study shows that: (i) skewed data labels are a fundamental and pervasive problem for decentralized learning, causing signifi- cant accuracy loss across many ...
Aug 19, 2020 · Abstract. Many large-scale machine learning (ML) applications need to perform decentralized learning over datasets.
Oct 1, 2019 · SkewScout is presented, a system-level approach that adapts the communication frequency of decentralized learning algorithms to the ...