Model Extraction Attacks on Split Federated Learning

Understand

Federated Learning (FL) is a popular collaborative learning scheme involving multiple clients and a server.

• FL focuses on protecting clients' data but turns out to be highly vulnerable to Intellectual Property (IP) threats.
• Since FL periodically collects and distributes the model parameters, a free-rider can download the latest model and thus steal model IP.
• Split Federated Learning (SFL), a recent variant of FL that supports training with resource-constrained clients, splits the model into two, giving one part of the model to clients (client-side model), and the remaining part to the server (server-side model).