On Pre-Training for Federated Learning
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In most of the literature on federated learning (FL), neural networks are initialized with random weights. In this paper, we present an empirical study on the effect of pre-training on FL. Specifically, we aim to investigate if pre-training can alleviate the drastic accuracy drop when clients' decentralized data are non-IID. We focus on FedAvg, the fundamental and most widely used FL algorithm. We found that pre-training does largely close the gap between FedAvg and centralized learning under non-IID data, but this does not come from alleviating the well-known model drifting problem in FedAvg's local training. Instead, how pre-training helps FedAvg is by making FedAvg's global aggregation more stable. When pre-training using real data is not feasible for FL, we propose a novel approach to pre-train with synthetic data. On various image datasets (including one for segmentation), our approach with synthetic pre-training leads to a notable gain, essentially a critical step toward scaling up federated learning for real-world applications.
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