A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates

We propose a novel framework to study asynchronous federated learning optimization with delays in gradient updates. Our theoretical framework extends the standard FedAvg aggregation scheme by introducing stochastic aggregation weights to represent the variability of the clients update time, due for example to heterogeneous hardware capabilities. Our formalism applies to the general federated setting where clients have heterogeneous datasets and perform at least one step of stochastic gradient descent (SGD). We demonstrate convergence for such a scheme and provide sufficient …

aggregation asynchronous convergence datasets example federated learning framework general gradient hardware least novel optimization standard stochastic study theory updates