Rendering Wireless Environments Useful for Gradient Estimators: A Zero-Order Stochastic Federated Learning Method

Federated learning (FL) is a novel approach to machine learning that allows multiple edge devices to collaboratively train a model without disclosing their raw data. However, several challenges hinder the practical implementation of this approach, especially when devices and the server communicate over wireless channels, as it suffers from communication and computation bottlenecks in this case. By utilizing a communication-efficient framework, we propose a novel zero-order (ZO) method with a one-point gradient estimator that harnesses the nature of the wireless …

challenges cs.dc cs.lg cs.ma data devices edge edge devices environments federated learning gradient hinder implementation machine machine learning math.oc multiple novel practical raw rendering server stochastic train wireless