Qiang Li

EIFL: Efficiently Protecting Global Model Privacy and Integrity Against an Untrusted Server in Federated Learning

Federated learning (FL) typically adopts a server-client architecture, where the server aggregates clients'local models (i.e., the input) and returns the aggregated global model (i.e., the output) to clients. An untrusted server may return a tampered global model to compromise the output integrity. Some existing scheme...