Federated Learning for Non-IID Data via Client Variance Reduction and Adaptive Server Update

• URL: http://arxiv.org/abs/2207.08391v1
• Date: Mon, 18 Jul 2022 05:58:19 GMT
• Title: Federated Learning for Non-IID Data via Client Variance Reduction and Adaptive Server Update
• Authors: Hiep Nguyen, Lam Phan, Harikrishna Warrier and Yogesh Gupta
• Abstract summary: Federated learning (FL) is an emerging technique used to collaboratively train a global machine learning model. The main obstacle to FL's practical implementation is the Non-Independent and Identical (Non-IID) data distribution across users. We propose a method (ComFed) that enhances the whole training process on both the client and server sides.
• Score: 5.161531917413708
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning (FL) is an emerging technique used to collaboratively train a global machine learning model while keeping the data localized on the user devices. The main obstacle to FL's practical implementation is the Non-Independent and Identical (Non-IID) data distribution across users, which slows convergence and degrades performance. To tackle this fundamental issue, we propose a method (ComFed) that enhances the whole training process on both the client and server sides. The key idea of ComFed is to simultaneously utilize client-variance reduction techniques to facilitate server aggregation and global adaptive update techniques to accelerate learning. Our experiments on the Cifar-10 classification task show that ComFed can improve state-of-the-art algorithms dedicated to Non-IID data.

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