semantic_scholarProceedings on Privacy Enhancing Technologies
SoK: Verifiable Integrity Claims for Privacy-Preserving Federated Learning
Andrea Rizzini, Marco Esposito, Tommaso Gagliardoni, F. Bruschi
TL;DR: This SoK model federated learning as an append-only transcript of submissions, admissions, aggregation, and finalization events, and formalize verifiability as a collection of integrity claims issued by clients and the aggregator, and checked by different verifier classes.
Federated Learning (FL) is an advancement in Machine Learning motivated by the need to preserve the privacy of the data used to train models. While it effectively addresses this issue, the multi-participant paradigm on which it is based introduces several challenges. Among these…