Differentially private federated deep learning for multi-site medical image segmentation

Understand

Collaborative machine learning techniques such as federated learning (FL) enable the training of models on effectively larger datasets without data transfer.

• Recent initiatives have demonstrated that segmentation models trained with FL can achieve performance similar to locally trained models.
• However, FL is not a fully privacy-preserving technique and privacy-centred attacks can disclose confidential patient data.
• Thus, supplementing FL with privacy-enhancing technologies (PTs) such as differential privacy (DP) is a requirement for clinical applications in a multi-institutional setting.