Application of Federated Averging algorithm to train LSTM models for time series forecasting. Notebook is written in french.
Libraries used in this repo:
sklearn: 0.24.2 pytorch: 1.9.0 numpy: 1.19.5 matplotlib: 3.2.2
See datasets directory to get the datasets used. In the scripts and notebook, a dataset is a multivariate time-series stored in a numpy array with size (T_size, n_variables), where T_size is the number of time steps (same along the time series components) and n_variables is the dimension of a single temporal sample.
Clément Lejeune.
Datasets from: H. F. Yu, N. Rao, and I. S. Dhillon, “Temporal regularized matrix factorization for high-dimensional time series prediction,” in NIPS, 2016, pp. 847–855.
Federated Averaging algorithm: H. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. Agüera y Arcas, “Communication-Efficient Learning of Deep Networks from Decentralized Data,” in AISTATS, 2017, vol. 54.