Fair and Privacy-Preserving Alzheimer's Disease Diagnosis Based on Spontaneous Speech Analysis via Federated Learning

Abstract

In this work, we propose the first federated-learning-based approach for achieving automatic AD diagnosis via spontaneous speech analysis while ensuring the subjects’ data privacy. To ensure fairness of the model performance across clients in federated settings, we further deploy fair aggregation mechanisms, particularly q-FEDAvg and q-FEDSgd, which greatly reduces the algorithmic biases due to the data heterogeneity among the clients.

Publication
In IEEE Engineering in Medicine and Biology Society 2022