  Federated learning (FL) is an emerging paradigm for training deep neural
networks (DNNs) in distributed manners. Current FL approaches all suffer from
high communication overhead and information leakage. In this work, we present a
federated learning algorithm based on evolution strategies (FedES), a
zeroth-order training method. Instead of transmitting model parameters, FedES
only communicates loss values, and thus has very low communication overhead.
Moreover, a third party is unable to estimate gradients without knowing the
pre-shared seed, which protects data privacy. Experimental results demonstrate
FedES can achieve the above benefits while keeping convergence performance the
same as that with back propagation methods.
