On-device learning enhances privacy and security by keeping data local, reducing the need for cloud-based processing. It provides real-time, personalized experiences by adapting to user behavior instantly without relying on constant connectivity. This approach is more efficient in terms of bandwidth and energy, making it ideal for resource-constrained environments.

Secure Multi-Party Computation (MPC) enables privacy-preserving machine learning by allowing multiple parties to collaboratively train models or perform inference on encrypted data without revealing their individual inputs. This ensures that sensitive data remains confidential while still benefiting from the collective computation. MPC-based approaches are useful in scenarios where data privacy is paramount, such as in healthcare or finance.

Deep Neural Network (DNN) inference on edge devices allows for real-time processing and decision-making directly on the device, without relying on cloud connectivity. This approach enhances privacy, reduces latency, and conserves bandwidth by keeping data and computations local. Despite the resource constraints of edge devices, optimizations like model compression and quantization make DNN inference feasible and efficient.

Applied machine learning involves using machine learning techniques to solve real-world problems by developing models that can learn from data and make predictions or decisions. It spans a wide range of applications, from recommendation systems and fraud detection to autonomous vehicles and natural language processing. The focus is on translating theoretical concepts into practical solutions that deliver tangible benefits in various industries.