FuSeFL: Fully Secure and Scalable Federated Learning

TL;DR

FuSeFL introduces a fully secure, scalable federated learning scheme that decentralizes training with lightweight MPC, ensuring confidentiality of data and models while significantly improving training efficiency.

Contribution

The paper proposes FuSeFL, a novel federated learning framework that decentralizes training and enhances security with lightweight MPC, reducing overhead and protecting global models.

Findings

Defends against inference and reconstruction attacks.

Achieves up to 13x faster training speed.

Uses 50% less server memory.

Abstract

Federated Learning (FL) enables collaborative model training without centralizing client data, making it attractive for privacy-sensitive domains. While existing approaches employ cryptographic techniques such as homomorphic encryption, differential privacy, or secure multiparty computation to mitigate inference attacks, including model inversion, membership inference, and gradient leakage, they often suffer from high computational and memory overheads. Moreover, many methods overlook the confidentiality of the global model itself, which may be proprietary and sensitive. These challenges limit the practicality of secure FL, especially in settings that involve large datasets and strict compliance requirements. We present FuSeFL, a Fully Secure and scalable FL scheme, which decentralizes training across client pairs using lightweight MPC, while confining the server's role to secure aggregation, client pairing, and routing. This design eliminates server bottlenecks, avoids full data offloading, and preserves full confidentiality of data, model, and updates throughout training. Based on our experiment, FuSeFL defends against unauthorized observation, reconstruction attacks, and inference attacks such as gradient leakage, membership inference, and inversion attacks, while achieving up to $13 \times$ speedup in training time and 50% lower server memory usage compared to our baseline.