Harnessing Inherent Noise

Harnessing inherent noise in computation and machine learning aims to leverage randomness, typically viewed as a hindrance, as a resource for improved performance, security, and privacy. Current research focuses on exploiting noise in diverse contexts, including federated learning (where strategic noise manipulation affects model aggregation), multi-robot exploration (where noise enhances exploration efficiency), and adversarial attack defense (where noise characteristics reveal malicious data manipulation). This approach offers significant potential for enhancing the robustness, security, and privacy of various machine learning models and algorithms, impacting fields from smart grids to neuromorphic computing.