Client transfers between servers

Accelerating Geo-distributed Learning with Client Transfers

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.

Flat Multi-Server

Asynchronous Multi-Server Federated Learning for Geo-Distributed Clients

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.