DRACO: Decentralized Asynchronous Federated Learning over Row-Stochastic Wireless Networks
This work addresses the problem of efficient and stable decentralized learning for smart IoT and Edge AI applications, representing an incremental improvement by introducing asynchronicity and autonomy in existing decentralized optimization methods.
The paper tackles the challenge of stable convergence in decentralized federated learning without strong assumptions on data distributions or update policies, proposing DRACO, a method for asynchronous SGD over row-stochastic wireless networks that enables continuous communication and eliminates synchronization, with numerical experiments confirming its efficacy.
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.