  Federated Learning (FL) is a distributed machine learning paradigm that
allows clients to train models on their data while preserving their privacy. FL
algorithms, such as Federated Averaging (FedAvg) and its variants, have been
shown to converge well in many scenarios. However, these methods require
clients to upload their local updates to the server in a synchronous manner,
which can be slow and unreliable in realistic FL settings. To address this
issue, researchers have developed asynchronous FL methods that allow clients to
continue training on their local data using a stale global model. However, most
of these methods simply aggregate all of the received updates without
considering their relative contributions, which can slow down convergence. In
this paper, we propose a contribution-aware asynchronous FL method that takes
into account the staleness and statistical heterogeneity of the received
updates. Our method dynamically adjusts the contribution of each update based
on these factors, which can speed up convergence compared to existing methods.
