Abstract

Federated learning (FL) has gained substantial attention in recent years due to 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 …

Metadata

publication

ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and …, 2023

year

2023

publication date

2023/6/4

authors

Tuo Zhang, Tiantian Feng, Samiul Alam, Sunwoo Lee, Mi Zhang, Shrikanth S Narayanan, Salman Avestimehr

link

https://ieeexplore.ieee.org/abstract/document/10096500/

resource_link

https://arxiv.org/pdf/2210.15707

conference

ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

pages

1-5

publisher

IEEE