Exploring Federated Self-Supervised Learning for General Purpose Audio
  Understanding
Yasar Abbas Ur Rehman Kin Wai Lau Yuyang Xie Lan Ma Jiajun Shen
The integration of Federated Learning (FL) and Self-supervised Learning (SSL)
offers a unique and synergetic combination to exploit the audio data for
general-purpose audio understanding, without compromising user data privacy.
However, rare efforts have been made to investigate the SSL models in the FL
regime for general-purpose audio understanding, especially when the training
data is generated by large-scale heterogeneous audio sources. In this paper, we
evaluate the performance of feature-matching and predictive audio-SSL
techniques when integrated into large-scale FL settings simulated with
non-independently identically distributed (non-iid) data. We propose a novel
Federated SSL (F-SSL) framework, dubbed FASSL, that enables learning
intermediate feature representations from large-scale decentralized
heterogeneous clients, holding unlabelled audio data. Our study has found that
audio F-SSL approaches perform on par with the centralized audio-SSL approaches
on the audio-retrieval task. Extensive experiments demonstrate the
effectiveness and significance of FASSL as it assists in obtaining the optimal
global model for state-of-the-art FL aggregation methods.