  Research on sound event detection (SED) in environmental settings has seen
increased attention in recent years. The large amounts of (private) domestic or
urban audio data needed raise significant logistical and privacy concerns. The
inherently distributed nature of these tasks, make federated learning (FL) a
promising approach to take advantage of largescale data while mitigating
privacy issues. While FL has also seen increased attention recently, to the
best of our knowledge there is no research towards FL for SED. To address this
gap and foster further research in this field, we create and publish novel FL
datasets for SED in domestic and urban environments. Furthermore, we provide
baseline results on the datasets in a FL context for three deep neural network
architectures. The results indicate that FL is a promising approach for SED,
but faces challenges with divergent data distributions inherent to distributed
client edge devices.
