Queuing dynamics of asynchronous Federated Learning
This work addresses inefficiencies in asynchronous federated learning for distributed systems, though it appears incremental as it builds on existing algorithms with a novel sampling approach.
The paper tackles the problem of asynchronous federated learning with nodes of varying computational speeds by proposing a non-uniform sampling scheme that accounts for queuing dynamics, resulting in significant improvements over state-of-the-art methods on an image classification task.
We study asynchronous federated learning mechanisms with nodes having potentially different computational speeds. In such an environment, each node is allowed to work on models with potential delays and contribute to updates to the central server at its own pace. Existing analyses of such algorithms typically depend on intractable quantities such as the maximum node delay and do not consider the underlying queuing dynamics of the system. In this paper, we propose a non-uniform sampling scheme for the central server that allows for lower delays with better complexity, taking into account the closed Jackson network structure of the associated computational graph. Our experiments clearly show a significant improvement of our method over current state-of-the-art asynchronous algorithms on an image classification problem.