Network Coding for Federated Learning Systems

Nowadays, artificial intelligence is limited by privacy and security problems. Compared with the ordinary machine learning, federated learning (FL) enables multiple participants to collaboratively learn a shared machine learning model while keeping all the training data on local devices. However, most of the current secured federated learning systems (FLSs) are built up with high computational and communication costs. On the other hand, optimizing the network structure of federated learning systems can reduce communication complexity by considering the correlation of the transmission channels.