Federated Learning Architectures and Communication-Efficient Optimisation for Privacy-Preserving Distributed AI Systems
Federated learning (FL) trains a shared model across data holders that cannot pool their records, but deployments remain bounded by three coupled costs: uplink traffic from repeated model exchange, accuracy loss under statistically heterogeneous clients, and the information that updates still leak. These are usually at...