Federated Learning with Non-IID Data
This is an implementation of the following paper:
Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, Vikas Chandra.
Federated Learning with Non-IID Data
arXiv:1806.00582.
TL;DR: Previous federated optization algorithms (such as FedAvg and FedProx) converge to stationary points of a mismatched objective function due to heterogeneity in data distribution. In this paper, the authors propose a data-sharing strategy to improve training on non-IID data by creating a small subset of data which is globally shared between all the edge devices.
Abstract: Federated learning enables resource-constrained edge compute devices, such as mobile phones and IoT devices, to learn a shared model for prediction, while keeping the