DRACO: Decentralized Asynchronous Federated Learning over Continuous Row-Stochastic Network Matrices

arXiv:2406.13533v1 Announce Type: new
Abstract: Recent developments and emerging use cases, such as smart Internet of Things (IoT) and Edge AI, have sparked considerable interest in the training of neural networks over fully decentralized (serverless) networks. One of the major challenges of decentralized learning is to ensure stable convergence without resorting to strong assumptions applied for each agent regarding data distributions or updating policies. To address these issues, we propose DRACO, a novel method for decentralized asynchronous Stochastic Gradient Descent …

abstract and edge ai arxiv asynchronous cases challenges continuous convergence cs.it cs.lg cs.ni decentralized edge edge ai federated learning internet internet of things iot major math.it network networks neural networks serverless smart stochastic things training type use cases