Impact of network topology on the performance of Decentralized Federated Learning

arXiv:2402.18606v1 Announce Type: new
Abstract: Fully decentralized learning is gaining momentum for training AI models at the Internet's edge, addressing infrastructure challenges and privacy concerns. In a decentralized machine learning system, data is distributed across multiple nodes, with each node training a local model based on its respective dataset. The local models are then shared and combined to form a global model capable of making accurate predictions on new data. Our exploration focuses on how different types of network structures …

abstract ai models arxiv challenges concerns cs.ai cs.dc cs.lg data dataset decentralized distributed edge federated learning impact infrastructure internet machine machine learning multiple network node nodes performance privacy topology training training ai training ai models type