Federated learning (FL) has been recently introduced to enable training deep learning (DL) models or AI agents without sharing the data. In other words, AI agents at local hubs, e.g. hospitals, are trained on their own data and only share the trained parameters with a centralized AI model or other AI agents. Leveraging such a massive amount of data in a privacy-preserved fashion adhering to the General Data Protection Regulation (GDPR) would have a great impact on medical diagnosis, outbreak detection, and other healthcare services.
Yet, principal challenges, to overcome, concern the nature of medical data, namely data heterogeneity; severe class-imbalance, few amounts of annotated data, inter-/intra-scanners variability (domain shift), inter-/intra- observer variability (noisy annotations); system heterogeneity, and explainability and robustness.
The mission of this Helmholtz AI young investigator group is to develop novel algorithms for a groundbreaking new generation of deep federated learning, which can learn to reCognize, AdapT, lEarn, Reason and exPlain, dIstiLl the knowledge and coLlAboRate with other AI agents (CATERPILLAR) in a robust and privacy-preserved fashion, to provide personalized healthcare services.