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  • title: Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning
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            Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning
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            Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning

            Dec 6, 2021

            Speakers

            SC

            Sen Cui

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            WP

            Weishen Pan

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            JL

            Jian Liang

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            About

            Federated learning (FL) has gain growing interests for its capability of learning from distributed data sources collectively without the need of accessing the raw data samples across different sources. So far FL research has mostly focused on improving the performance, how the algorithmic disparity will be impacted for the model learned from FL and the impact of algorithmic disparity on the utility inconsistency are largely unexplored. In this paper, we propose an FL framework to jointly conside…

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            NeurIPS 2021

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