Large Scale Deployment

Large-scale deployment focuses on effectively utilizing and managing systems across numerous locations or devices, addressing challenges in data transfer, coordination, and resource allocation. Current research emphasizes efficient algorithms for distributed learning (like federated learning), robust anomaly detection methods, and strategies for mitigating risks associated with widespread deployment, such as those arising from malicious actors or environmental factors. This research is crucial for advancing applications ranging from environmental monitoring (e.g., underwater gliders) and medical image analysis to cybersecurity and the development of more resilient AI systems. The ultimate goal is to enable the reliable and scalable operation of complex systems across diverse and potentially challenging environments.