  Federated learning (FL) enables multiple clients to jointly train a global
model under the coordination of a central server. Although FL is a
privacy-aware paradigm, where raw data sharing is not required, recent studies
have shown that FL might leak the private data of a client through the model
parameters shared with the server or the other clients. In this paper, we
present the HyFed framework, which enhances the privacy of FL while preserving
the utility of the global model. HyFed provides developers with a generic API
to develop federated, privacy-preserving algorithms. HyFed supports both
simulation and federated operation modes and its source code is publicly
available at https://github.com/tum-aimed/hyfed.
