SLVR: Securely Leveraging Client Validation for Robust Federated Learning

Federated Learning (FL) enables collaborative model training while keeping client data private. However, exposing individual client updates makes FL vulnerable to reconstruction attacks. Secure aggregation mitigates such privacy risks but prevents the server from verifying the validity of each client update, creating a privacy-robustness trade-off. Recent efforts attempt to address this trade-off by enforcing model integrity checks on client updates using privacy-enhancing technologies, but they support limited predicates and often depend on public validation data. We propose SLVR, a general framework that securely leverages clients’ private data through secure multi-party computation. SLVR enables stronger, data-driven integrity validation that takes clients’ private data as input while removing the need for public validation data. Our empirical evaluations show that SLVR improves robustness against model poisoning attacks, particularly outperforming existing methods by up to 60% under adaptive attacks.