Scale-Robust Timely Asynchronous Decentralized Learning

arXiv:2404.19749v1 Announce Type: cross
Abstract: We consider an asynchronous decentralized learning system, which consists of a network of connected devices trying to learn a machine learning model without any centralized parameter server. The users in the network have their own local training data, which is used for learning across all the nodes in the network. The learning method consists of two processes, evolving simultaneously without any necessary synchronization. The first process is the model update, where the users update their …

abstract arxiv asynchronous connected devices cs.it cs.lg cs.ma cs.ni data decentralized devices eess.sp learn machine machine learning machine learning model math.it network nodes robust scale server training training data type