Optimization Trade-offs in Asynchronous Federated Learning: A Stochastic Networks Approach

• URL: http://arxiv.org/abs/2603.26231v1
• Date: Fri, 27 Mar 2026 09:53:53 GMT
• Title: Optimization Trade-offs in Asynchronous Federated Learning: A Stochastic Networks Approach
• Abstract summary: Asynchronous algorithms increase the update throughput by processing updates upon arrival.<n> gradient staleness, which degrades convergence, and bias toward faster clients under heterogeneous data.<n>We develop a queue-network framework for Generalized AsyncSGD that jointly models random computation times at the clients and the central server.
• Score: 2.0200069355804264
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Synchronous federated learning scales poorly due to the straggler effect. Asynchronous algorithms increase the update throughput by processing updates upon arrival, but they introduce two fundamental challenges: gradient staleness, which degrades convergence, and bias toward faster clients under heterogeneous data distributions. Although algorithms such as AsyncSGD and Generalized AsyncSGD mitigate this bias via client-side task queues, most existing analyses neglect the underlying queueing dynamics and lack closed-form characterizations of the update throughput and gradient staleness. To close this gap, we develop a stochastic queueing-network framework for Generalized AsyncSGD that jointly models random computation times at the clients and the central server, as well as random uplink and downlink communication delays. Leveraging product-form network theory, we derive a closed-form expression for the update throughput, alongside closed-form upper bounds for both the communication round complexity and the expected wall-clock time required to reach an $ε$-stationary point. These results formally characterize the trade-off between gradient staleness and wall-clock convergence speed. We further extend the framework to quantify energy consumption under stochastic timing, revealing an additional trade-off between convergence speed and energy efficiency. Building on these analytical results, we propose gradient-based optimization strategies to jointly optimize routing and concurrency. Experiments on EMNIST demonstrate reductions of 29%--46% in convergence time and 36%--49% in energy consumption compared to AsyncSGD.

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