AnImprovedAnalysisofGradientTracking forDecentralizedMachineLearning

Methods that train machine learning models on decentralized data offer many advantages over traditional centralized approaches in core aspects such as data ownership, privacy, fault tolerance and scalability [12,33]. Manycurrent efforts inthis direction come under the banner offederated learning [17, 29, 28, 12], where a central entity orchestrates the training and collects aggregate updates from the participating devices.