According to a recent press release, “To help advance medical research while preserving data privacy and improving patient outcomes for brain tumor identification, NVIDIA researchers in collaboration with King’s College London researchers today announced the introduction of the first privacy-preserving federated learning system for medical image analysis. NVIDIA is working with King’s College London and French startup Owkin to enable federated learning for the newly established London Medical Imaging and AI Centre for Value Based Healthcare.”
The release continues, “Federated learning is a learning paradigm that allows developers and organizations to train a centralized deep neural network (DNN) with training data distributed across multiple locations. This makes it possible for organizations to collaborate on a shared model, without needing to directly share any clinical data. Dr. Jorge Cardoso, a co-author of this paper and associate professor in AI at King’s College London, and Abdul Hamid Halabi, Global Business Development Lead, Healthcare & Life Sciences at NVIDIA, describe the work and Federated Learning. ‘Federated learning allows collaborative and decentralized training of neural networks without sharing the patient data,’ the researchers stated in their paper. ‘Each node trains its own local model and, periodically, submits it to a parameter server. The server accumulates and aggregates the individual contributions to yield a global model, which is then shared with all nodes’.”
The release adds, “Although federated learning can provide high security in terms of privacy, there are still ways to reconstruct data by model inversion, the researchers explained. To help make federated learning even safer, the researchers investigate the feasibility of using the ε-differential privacy framework, a way to formally define privacy loss, to protect patient and institutional data with a strong privacy guarantee. To ensure patient privacy is priority, differential privacy and other state-of-the-art privacy protection techniques are being built into the Owkin architecture.”
Read more at nvidia.com.
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