PRIVACY-ENHANCED FEDERATED LEARNING AGAINST ATTRIBUTE INFERENCE ATTACK FOR SPEECH EMOTION RECOGNITION

Federal learning-based (FL) Speech Emotion Recognition (SER) framework aims to protect data privacy when characterizing emotions. However, previous studies have shown that the framework is vulnerable, because curious servers can indirectly infer user private information. To address this challenge, we propose a novel privacy-enhanced SER approach against attribute inference attack. It helps filter sensitive information and attends to highlight emotion features before uploading the shared model updates under the FL. Firstly, a bi-directional recurrent neural network captures the latent representations in sequences to discard partial redundant features. Then, a feature attention mechanism is applied to focus on the salient regions in the latent representations, further hiding emotion-irrelevant attributes. The experimental results show that the introduced model is effective. The attack capability of a gender prediction model is reduced to a chance level while retaining SER performance.