Exploring Federated Self-Supervised Learning for General Purpose Audio Understanding
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Federated LearningRetrievalSelf-Supervised LearningSimilar Papers 제목 키워드 기반
Boosting multi-demographic federated learning for chest x-ray analysis using general-purpose self-supervised representations
Reliable artificial intelligence (AI) models for medical image analysis often depend on large and diverse labeled datasets. Federated learning (FL) offers a decentralized and privacy-preserving approach to training but s…
Federated LearningMedical Image AnalysisPrivacy PreservingTransfer LearningFederated Self-Supervised Learning for One-Shot Cross-Modal and Cross-Imaging Technique Segmentation
Decentralized federated learning enables learning of data representations from multiple sources without compromising the privacy of the clients. In applications like medical image segmentation, where obtaining a large an…
Federated LearningImage SegmentationMedical Image SegmentationOne-Shot Segmentation+3Less Forgetting for Better Generalization: Exploring Continual-learning Fine-tuning Methods for Speech Self-supervised Representations
Despite being trained on massive and diverse datasets, speech self-supervised encoders are generally used for downstream purposes as mere frozen feature extractors or model initializers before fine-tuning. The former sev…
Continual LearningDomain Generalizationspeech-recognitionSpeech RecognitionExploring Machine Learning Models for Federated Learning: A Review of Approaches, Performance, and Limitations
In the growing world of artificial intelligence, federated learning is a distributed learning framework enhanced to preserve the privacy of individuals' data. Federated learning lays the groundwork for collaborative rese…
Decision MakingFederated LearningPrivacy PreservingExploring Pre-trained General-purpose Audio Representations for Heart Murmur Detection
To reduce the need for skilled clinicians in heart sound interpretation, recent studies on automating cardiac auscultation have explored deep learning approaches. However, despite the demands for large data for deep lear…
Classify murmursGPUSelf-Supervised LearningTransfer Learning