Communication and Energy-Efficient Federated Learning using a Zero-Order Optimization Technique

A novel zero-order optimization method is proposed for federated learning that requires the upload of a quantized single scalar per iteration by each device instead of the whole gradient vector, significantly reducing communication overhead and energy consumption.