Resource Allocation for Federated Learning over Wireless Networks

This thesis examines resource allocation for Federated Learning in wireless networks. In Federated learning a server and a number of users exchange neural network parameters during training. This thesis aims to create a realistic simulation of a Federated Learning process by creating a channel model and using compression when channel capacity is insufficient. In the thesis we learn that Federated learning can handle high ratios of sparsification compression. We will also investigate how the choice of users and scheduling schemes affect the convergence speed and accuracy of the training process. This thesis will conclude that the choice of scheduling schemes will depend on the distributed data distribution