Vidyut discusses the challenges of optimizing hardware for graph computing, emphasizing the need for better memory access patterns. He highlights the journey of federated learning, focusing on initial inferencing on devices while gradually addressing algorithmic elements like differential privacy. The conversation reveals the potential for future advancements in on-device training, though it remains a distant goal.
