Prasun Chakrabarti

Abstract The use of image-sensing and real-time processing in Intelligent Transportation Systems (ITS) has introduced a sudden surge in transmitting and gathering high-resolution visual data from vehicle cameras, road infrastructures, and user devices. Such image data are, however, exceedingly susceptible to interception, tampering, and privacy breaches with regard to imminent quantum computing attacks that can break classical encryption algorithms.

Abstract Event-based cameras provide high-temporal-resolution motion information and wide dynamic range, making them suitable for depth estimation in challenging dynamic environments. However, existing depth learning approaches often rely on ground-truth supervision, handcrafted event representations, or computationally expensive fusion strategies, limiting scalability and real-time deployment.

Abstract With an increasing emphasis on user data control and privacy regulations, such as the General Data Protection Regulation (GDPR), machine unlearning (MU) has emerged as a crucial mechanism for managing data in AI systems. MU enables models to remove the influence of specific user data upon request. This problem becomes more complex in collaborative settings such as Federated Learning (FL), where data remains distributed across multiple clients, giving rise to Federated Unlearning (FU).