Nomad is the first dynamic client transfer framework for multi-server FL, reallocating clients based on network conditions and data alignment to reduce latency and improve learning. Unlike static assignments, Nomad enables flexible migration during training. Experiments show accuracy improvements of up to 31.8 points in join-only settings and 18.8 points under churn, consistently surpassing strong baselines and scaling well across geographic deployments.

We introduce a federated diffusion framework that allows independent, privacy-preserving training of DDPMs without exposing local data. By adapting FedAvg and leveraging the UNet backbone efficiently, our method cuts parameter exchange by up to 74% compared to naive FedAvg, while preserving image quality close to centralized training, as measured by FID.

To speed up the Federated Learning process, learning tasks can be offloaded to other clients. Using similarity metrics and a resource aware scheduler, we are able to speed up the training process for Federated Learning.