Federated deep learning enables collaboration to share data, while preserving data privacy, to build better AI models with reduced bias. This technique has wide applications across health care, finance, services, manufacturing, energy, and other industries. Supermicro will describe an implementation of federated deep learning using Supermicro AI platforms for health care and life sciences, using a resilient architecture built with MONAI, NVIDIA FLARE, NVIDIA CLARA, Kubernetes, and CEPH. The implementation ran multi-day AI training of variational network models using fMRI data from multiple sources, while preserving data privacy. Supermicro presents choices of systems that support deep learning training, inference, Omniverse, virtualization, visualization, Kubernetes, and CEPH. These systems provide the latest NVIDIA technologies, offering form factors, power consumption, and cooling for system builders and IT architects to make optimal trade-offs to build their deep learning infrastructure.