Abstract
Federated learning is a emerging branch of machine learning research, that is examining the methods for training models over geographically separated, unbalanced and non-iid data. In FL, on non-convex problems, as in single node training, the almost exclusively used method is mini batch gradient descent. In this work we examine the effect of using stateful training method in a federated environment. According to our empirical results with these methods, at the cost of synchronizing state variables along with model parameters, a significant improvement can be achieved.
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Notes
- 1.
Hyper-parameters are denoted following Keras documentation https://keras.io/api/optimizers/.
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Acknowledgements
Project no. ED_18-1-2019-0030 (Application domain specific highly reliable IT solutions subprogramme) has been implemented with the support provided from the National Research, Development and Innovation Fund of Hungary, financed under the Thematic Excellence Programme funding scheme.
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Kiss, P., Horváth, T., Felbab, V. (2020). Stateful Optimization in Federated Learning of Neural Networks. In: Analide, C., Novais, P., Camacho, D., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2020. IDEAL 2020. Lecture Notes in Computer Science(), vol 12490. Springer, Cham. https://doi.org/10.1007/978-3-030-62365-4_33
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