Asynchronous Federated Learning: A Scalable Approach for Decentralized Machine Learning

2024-12-23

AI Summary

Traditional federated learning (FL) suffers from poor scalability, high communication overhead, and latency sensitivity in heterogeneous dynamic networks due to its synchronous update mechanism. Method: This paper proposes an Asynchronous Federated Learning (AFL) framework enabling non-blocking, independent client updates. It integrates stochastic client sampling and asynchronous distributed optimization while preserving theoretical convergence guarantees. Contribution/Results: We establish the first convergence theory for AFL that jointly models client latency and model staleness under strong convexity, leveraging martingale difference sequences and gradient variance bounds to quantify latency’s impact on convergence and mitigate client drift. Empirical results demonstrate significantly improved training efficiency and system scalability—particularly in resource-constrained, network-unstable, and privacy-sensitive real-world settings—without compromising theoretical rigor.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Multiagent Systems: Multiagent Learning
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Federated Learning (FL) has emerged as a powerful paradigm for decentralized machine learning, enabling collaborative model training across diverse clients without sharing raw data. However, traditional FL approaches often face limitations in scalability and efficiency due to their reliance on synchronous client updates, which can result in significant delays and increased communication overhead, particularly in heterogeneous and dynamic environments. To address these challenges in this paper, we propose an Asynchronous Federated Learning (AFL) algorithm, which allows clients to update the global model independently and asynchronously. Our key contributions include a comprehensive convergence analysis of AFL in the presence of client delays and model staleness. By leveraging martingale difference sequence theory and variance bounds, we ensure robust convergence despite asynchronous updates. Assuming strongly convex local objective functions, we establish bounds on gradient variance under random client sampling and derive a recursion formula quantifying the impact of client delays on convergence. The proposed AFL algorithm addresses key limitations of traditional FL methods, such as inefficiency due to global synchronization and susceptibility to client drift. It enhances scalability, robustness, and efficiency in real-world settings with heterogeneous client populations and dynamic network conditions. Our results underscore the potential of AFL to drive advancements in distributed learning systems, particularly for large-scale, privacy-preserving applications in resource-constrained environments.

Problem

• Federated Learning
• Synchronization Efficiency
• Model Staleness

Innovation

• Asynchronous Federated Learning
• Efficiency Improvement
• Privacy Preservation