  Federated learning (FL) aims to train machine learning (ML) models across
potentially millions of edge client devices. Yet, training and customizing
models for FL clients is notoriously challenging due to the heterogeneity of
client data, device capabilities, and the massive scale of clients, making
individualized model exploration prohibitively expensive. State-of-the-art FL
solutions personalize a globally trained model or concurrently train multiple
models, but they often incur suboptimal model accuracy and huge training costs.
  In this paper, we introduce FedTrans, a multi-model FL training framework
that automatically produces and trains high-accuracy, hardware-compatible
models for individual clients at scale. FedTrans begins with a basic global
model, identifies accuracy bottlenecks in model architectures during training,
and then employs model transformation to derive new models for heterogeneous
clients on the fly. It judiciously assigns models to individual clients while
performing soft aggregation on multi-model updates to minimize total training
costs. Our evaluations using realistic settings show that FedTrans improves
individual client model accuracy by 14% - 72% while slashing training costs by
1.6X - 20X over state-of-the-art solutions.
