  Federated learning (FL) enables a set of geographically distributed clients
to collectively train a model through a server. Classically, the training
process is synchronous, but can be made asynchronous to maintain its speed in
presence of slow clients and in heterogeneous networks. The vast majority of
Byzantine fault-tolerant FL systems however rely on a synchronous training
process. Our solution is one of the first Byzantine-resilient and asynchronous
FL algorithms that does not require an auxiliary server dataset and is not
delayed by stragglers, which are shortcomings of previous works. Intuitively,
the server in our solution waits to receive a minimum number of updates from
clients on its latest model to safely update it, and is later able to safely
leverage the updates that late clients might send. We compare the performance
of our solution with state-of-the-art algorithms on both image and text
datasets under gradient inversion, perturbation, and backdoor attacks. Our
results indicate that our solution trains a model faster than previous
synchronous FL solution, and maintains a higher accuracy, up to 1.54x and up to
1.75x for perturbation and gradient inversion attacks respectively, in the
presence of Byzantine clients than previous asynchronous FL solutions.
