Exploring Federated Self-Supervised Learning for General Purpose Audio Understanding

• URL: http://arxiv.org/abs/2402.02889v1
• Date: Mon, 5 Feb 2024 10:57:48 GMT
• Title: Exploring Federated Self-Supervised Learning for General Purpose Audio Understanding
• Authors: Yasar Abbas Ur Rehman, Kin Wai Lau, Yuyang Xie, Lan Ma, Jiajun Shen
• Abstract summary: We propose a novel Federated SSL (F-SSL) framework, dubbed FASSL, that enables learning intermediate feature representations from large-scale decentralized heterogeneous clients. Our study has found that audio F-SSL approaches perform on par with the centralized audio-SSL approaches on the audio-retrieval task.
• Score: 14.468870364990291
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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