Related papers: Preconditioned Federated Learning

Preconditioned Federated Learning

URL: http://arxiv.org/abs/2309.11378v1
Date: Wed, 20 Sep 2023 14:58:47 GMT
Title: Preconditioned Federated Learning
Authors: Zeyi Tao, Jindi Wu, Qun Li
Abstract summary: Federated Learning (FL) is a distributed machine learning approach that enables model training in communication efficient and privacy-preserving manner. FedAvg has been considered to lack algorithm adaptivity compared to modern first-order adaptive optimizations. We propose new communication-efficient FL algortithms based on two adaptive frameworks: local adaptivity (PreFed) and server-side adaptivity (PreFedOp)
Score: 7.7269332266153326
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Federated Learning (FL) is a distributed machine learning approach that enables model training in communication efficient and privacy-preserving manner. The standard optimization method in FL is Federated Averaging (FedAvg), which performs multiple local SGD steps between communication rounds. FedAvg has been considered to lack algorithm adaptivity compared to modern first-order adaptive optimizations. In this paper, we propose new communication-efficient FL algortithms based on two adaptive frameworks: local adaptivity (PreFed) and server-side adaptivity (PreFedOp). Proposed methods adopt adaptivity by using a novel covariance matrix preconditioner. Theoretically, we provide convergence guarantees for our algorithms. The empirical experiments show our methods achieve state-of-the-art performances on both i.i.d. and non-i.i.d. settings.

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