Fortress: Multi-Level Secure and Efficient Distributed Learning

Summary

Fortress is a distributed learning framework that combines three defenses against gradient-level reconstruction, model-level inference, and training-level manipulation. It implements secure aggregation through dual-server Boolean secret sharing, adds differential privacy by adding noise directly to secret shares via polynomial approximation, and uses lightweight transcript-based verification for malicious resilience. Evaluation across multiple datasets reports communication overhead reduced by 6.2× to 32.3× while robustness under malicious attacks is maintained.