  Federated learning (FL) has gained substantial attention in recent years due
to the data privacy concerns related to the pervasiveness of consumer devices
that continuously collect data from users. While a number of FL benchmarks have
been developed to facilitate FL research, none of them include audio data and
audio-related tasks. In this paper, we fill this critical gap by introducing a
new FL benchmark for audio tasks which we refer to as FedAudio. FedAudio
includes four representative and commonly used audio datasets from three
important audio tasks that are well aligned with FL use cases. In particular, a
unique contribution of FedAudio is the introduction of data noises and label
errors to the datasets to emulate challenges when deploying FL systems in
real-world settings. FedAudio also includes the benchmark results of the
datasets and a PyTorch library with the objective of facilitating researchers
to fairly compare their algorithms. We hope FedAudio could act as a catalyst to
inspire new FL research for audio tasks and thus benefit the acoustic and
speech research community. The datasets and benchmark results can be accessed
at https://github.com/zhang-tuo-pdf/FedAudio.
