The Good,The Bad, and The Ugly: Quality Inference in Federated Learning

Federated learning is the most popular collaborative learning paradigm, where the learning is done locally; hence no private data is leaving the contributors' hardware. Yet, it might still leak some information, as for scalability reasons, only a subset of the participants contribute in each training round. In this paper we investigated whether it is possible to reconstruct the quality ordering of the training datasets.