  Standard acoustic event classification (AEC) solutions require large-scale
collection of data from client devices for model optimization. Federated
learning (FL) is a compelling framework that decouples data collection and
model training to enhance customer privacy. In this work, we investigate the
feasibility of applying FL to improve AEC performance while no customer data
can be directly uploaded to the server. We assume no pseudo labels can be
inferred from on-device user inputs, aligning with the typical use cases of
AEC. We adapt self-supervised learning to the FL framework for on-device
continual learning of representations, and it results in improved performance
of the downstream AEC classifiers without labeled/pseudo-labeled data
available. Compared to the baseline w/o FL, the proposed method improves
precision up to 20.3\% relatively while maintaining the recall. Our work
differs from prior work in FL in that our approach does not require
user-generated learning targets, and the data we use is collected from our Beta
program and is de-identified, to maximally simulate the production settings.
