Modern AI and machine learning systems are increasingly trained and deployed on distributed infrastructures consisting of multiple servers working together. While distributed computing enables larger models and faster processing, it also introduces new security challenges. Communication between nodes, shared resources, and distributed coordination mechanisms can create vulnerabilities that may not exist in single-machine systems. The goal of this project is to understand and evaluate security risks that arise when training or running AI/ML models in distributed environments. By identifying and studying these vulnerabilities, we can help build more secure and trustworthy AI systems.
Tags: Networks, Operating Systems, Artificial Intelligence, Machine Learning, Security, Systems, All Years