  In this work, we propose FedSSO, a server-side second-order optimization
method for federated learning (FL). In contrast to previous works in this
direction, we employ a server-side approximation for the Quasi-Newton method
without requiring any training data from the clients. In this way, we not only
shift the computation burden from clients to server, but also eliminate the
additional communication for second-order updates between clients and server
entirely. We provide theoretical guarantee for convergence of our novel method,
and empirically demonstrate our fast convergence and communication savings in
both convex and non-convex settings.
