Optimizing Asynchronous Federated Learning: A Delicate Trade-Off Between Model-Parameter Staleness and Update Frequency

2025-02-12

AI Summary

This paper addresses the inherent trade-off between model parameter staleness and update frequency in asynchronous federated learning, aiming to jointly optimize convergence accuracy and system efficiency. Methodologically, it first derives a discrete-time variant of Little’s Law to quantify relative staleness; second, it formulates a unified, differentiable upper bound that jointly incorporates staleness and throughput—overcoming the limitations of conventional single-objective optimization; and third, it designs a co-optimization algorithm grounded in stochastic modeling, queueing theory, and gradient convergence analysis. Experimental results across diverse scenarios demonstrate that the proposed framework improves model accuracy by 10–30% while significantly enhancing the accuracy–efficiency trade-off.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Search and Optimization: Learning to Search
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

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

Abstract

Synchronous federated learning (FL) scales poorly with the number of clients due to the straggler effect. Algorithms like FedAsync and GeneralizedFedAsync address this limitation by enabling asynchronous communication between clients and the central server. In this work, we rely on stochastic modeling to better understand the impact of design choices in asynchronous FL algorithms, such as the concurrency level and routing probabilities, and we leverage this knowledge to optimize loss. We characterize in particular a fundamental trade-off for optimizing asynchronous FL: minimizing gradient estimation errors by avoiding model parameter staleness, while also speeding up the system by increasing the throughput of model updates. Our two main contributions can be summarized as follows. First, we prove a discrete variant of Little's law to derive a closed-form expression for relative delay, a metric that quantifies staleness. This allows us to efficiently minimize the average loss per model update, which has been the gold standard in literature to date. Second, we observe that naively optimizing this metric leads us to slow down the system drastically by overemphazing staleness at the detriment of throughput. This motivates us to introduce an alternative metric that also takes system speed into account, for which we derive a tractable upper-bound that can be minimized numerically. Extensive numerical results show that these optimizations enhance accuracy by 10% to 30%.

Problem

• Optimize asynchronous federated learning efficiency
• Balance model staleness and update frequency
• Minimize gradient errors while maximizing system throughput

Innovation

• Asynchronous Federated Learning optimization
• Stochastic modeling for design choices
• Trade-off between staleness and throughput