Fortress: Multi-Level Secure and Efficient Distributed Learning

Distributed learning (training AI models across multiple computers while keeping data in different locations) faces three types of security threats: attackers reconstructing data from gradients (mathematical updates), inferring information from the final model, and manipulating the training process. Fortress is a framework that combines three defensive techniques—secure aggregation (encrypted combination of data), differential privacy (adding noise to protect individual data), and malicious resilience (verification to catch cheating)—while reducing communication overhead by 6.2× to 32.3× compared to existing approaches.