Learning of Deep Networks from Decentralized Data

Communication-Efficient Learning of Deep Networks from Decentralized Data. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, Blaise Aguera y Arcas. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics , PMLR 54:1273-1282, 2017.. The FL algorithm is FedAvg, based on the paper Communication-Efficient Learning of Deep Networks from Decentralized Data. Each client trains local model by DP-SGD [2] to perturb model parameters. Each client trains local model by DP-SGD [2] to perturb model parameters.

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Communication-efficient learning of deep networks from decentralized data HB. McMahan, E. Moore, D. Ramage. arXiv preprint 2016 [] Key Areas Distributed Artificial IntelligencModern mobile devices have access to a wealth of data suitable for learning models, which in turn can greatly improve the user experience on the device.. Figure 2: Test set accuracy vs. communication rounds for the MNIST CNN (IID and then pathological non-IID) and Shakespeare LSTM (IID and then by Play&Role) with C = 0.1 and optimized η. The gray lines show the target accuracies used in Table 2. Plots for the 2NN are given as Figure 7 in Appendix A. - "Communication-Efficient Learning of Deep Networks from Decentralized Data"