Federated and Decentralized Learning

Hospitals, phones and banks hold the data that would train the best models, and none of them are allowed to share it. This path builds the field that trains on data you never see: the FedAvg round structure and why local steps are the communication lever, the client drift that heterogeneous data causes and the control variates that correct it, gossip protocols that remove the server entirely and the spectral gap that governs them, and what model updates actually leak once someone attacks them.