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Ying Huang

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Proceedings Article | 10 November 2020 Paper

Federated learning with auxiliary generator

Ying Huang, HengJie Song

Proceedings Volume 11584, 115841P (2020) https://doi.org/10.1117/12.2580498

KEYWORDS: Data modeling, Gallium nitride, Machine learning, Statistical modeling, Performance modeling, Visualization, Neural networks, Image classification, Goggles

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Federated Learning (FL) is a client-based distributed machine learning framework, which aim to train a central- ized model with protecting data privacy. However, the decentralized datasets pose a challenge on the traditional FL, as they are non-independent and identical distributed (non-IID). Non-IID settings can result in client result gradient biases, which may decrease the accuracy of the model. To address this issue, we propose Federated Learning with Auxiliary Generator(FedGen), which keeps the consistent of data distribution between clients leveraging the auxiliary generator, and the gradient become more accurate. To demonstrate the effectiveness of proposed method, extensive experiments are conducted on the benchmark datasets, including the MNIST and LEAF dataset. The experimental results shows that FedGen converges 1.2 times faster than FedAvg, while the accuracy can be increased.

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