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.

通过将联邦学习（FL）与自监督学习（SSL）相结合，可以在不损害用户数据隐私的情况下利用音频数据进行通用音频理解。本文评估了在大规模非独立同分布（non-iid）数据生成的情况下，将特征匹配和预测式音频自监督学习技术与 FL 相结合的性能，提出一种名为 FASSL 的新型联邦自监督学习（F-SSL）框架，并通过广泛实验证明了 FASSL 的有效性和重要性，从而有助于获得最佳的全局模型。