Authors: Yasar Abbas Ur Rehman, Kin Wai Lau, Yuyang Xie, Lan Ma, Jiajun Shen

Published on: February 05, 2024

Impact Score: 8.45

Arxiv code: Arxiv:2402.02889

Summary

• What is new: The novel integration of Federated Learning with Self-supervised Learning for general-purpose audio understanding from large-scale, decentralized, and heterogeneous sources without compromising data privacy.
• Why this is important: There’s limited research on the effectiveness of Self-supervised Learning models in Federated Learning settings for audio understanding, particularly with non-iid, large-scale, heterogeneous audio data.
• What the research proposes: A new framework, FASSL, that combines Federated Learning and Self-supervised Learning to learn intermediate feature representations from unlabeled audio data across large-scale decentralized networks.
• Results: FASSL shows competitive performance with traditional centralized Self-supervised Learning methods on audio retrieval tasks, proving the synergy between Federated Learning and Self-supervised Learning.

Technical Details

Technological frameworks used: FASSL

Models used: Feature-matching and predictive audio-SSL techniques

Data used: Large-scale decentralized heterogeneous audio data

Potential Impact

Audio technology companies, streaming services, and privacy-focused application providers could significantly benefit or face disruption from the adoption of FASSL insights.

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