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FedSSO: A Federated Server-Side Second-Order Optimization Algorithm
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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.
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Cited by 1 Pith paper
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Accelerated Training of Federated Learning via Second-Order Methods
A survey that categorizes second-order federated learning methods and argues they reduce communication rounds, based on results borrowed from the cited papers rather than new experiments.
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