Mario: Multi-round Multiple-Aggregator Secure Aggregation with Robustness against Malicious Actors
Trung Son Nguyen, Tancrède Lepoint, Nguyen Trieu · 2025
Federated Learning (FL) enables multiple clients to collaboratively train a machine learning model while keeping their data private, eliminating the need for data sharing. Two common approaches to secure aggregation (SA) in FL are the single-aggregator and multiple-aggregator models. This work focuses on improving the multiple-aggregator model.Existing multiple-aggregator protocols such as Prio (NSDI 2017), Prio+ (SCN 2022), Elsa (S&P 2023) either offer robustness only in the presence of semi-honest servers or provide security without robustness and are limited to two aggregators. We introduce Mario, the first multiple-aggregator Secure Aggregation protocol that is both secure and robust in a malicious setting. Similar to prior work of Prio and Prio+, Mario provides secure aggregation in a setup of n servers and m clients. Unlike previous work, Mario removes the assumption of semi-honest servers, and provides a complete protocol with robustness under malicious clients and malicious servers. Our implementation shows that Mario is 3.40× and 283.4× faster than Elsa and Prio+, respecitively.