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

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...

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Bibliographic Details
Published inarXiv.org
Main Authors Jeong, Eunjeong, Kountouris, Marios
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 19.06.2024
Subjects
Algorithms
Autonomy
Convergence
Decoupling
Federated learning
Internet of Things
Machine learning
Neural networks
Wireless networks
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Summary: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 (SGD) over row-stochastic gossip wireless networks by leveraging continuous communication. Our approach enables edge devices within decentralized networks to perform local training and model exchanging along a continuous timeline, thereby eliminating the necessity for synchronized timing. The algorithm also features a specific technique of decoupling communication and computation schedules, which empowers complete autonomy for all users and manageable instructions for stragglers. Through a comprehensive convergence analysis, we highlight the advantages of asynchronous and autonomous participation in decentralized optimization. Our numerical experiments corroborate the efficacy of the proposed technique.
ISSN:2331-8422