Federated learning provides a framework for multiple participants to collectively train a neural network while maintaining data privacy, and is commonly achieved through homomorphic encryption.

The growth of the Internet of Things and embedded devices requires increasing the secure and energy-efficient communication protocols, whereas conventional cryptographic algorithms usually tend to be ...