FedDC: Efficient protection scheme based on chaotic system in federated learning

This research paper proposes FedDC, a protection scheme designed to secure federated learning (a training method where multiple computers train an AI model together without sharing raw data) by using a chaotic system (a mathematical approach based on unpredictable behavior). The scheme aims to make federated learning more efficient while protecting the privacy and security of the distributed training process.