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 propose an asynchronous, Byzantine-resilient FL algorithm that avoids straggler delays and requires no server dataset. By updating after a safe number of client contributions, it outperforms state-of-the-art methods, achieving faster training and higher accuracy under multiple attack types.

Spyker is the first fully asynchronous multi-server FL system, eliminating server idle time and single-server bottlenecks. Clients communicate only with their nearest server, while servers also update each other asynchronously. This continuously active design improves scalability and performance across MNIST, CIFAR-10, and WikiText-2.