The talk will explore privacy in machine learning (ML) and how Federated Learning (FL) can be leveraged to address privacy concerns effectively. Federated Learning allows training machine learning models across multiple decentralized devices or servers holding local data samples without exchanging them.

He will demonstrate a real-time Federated Learning setup using Python (MPI for communication), showcasing the integration of advanced communication protocols to manage real-time interaction and parallel processing among the nodes.

It will also discuss how model compression techniques (like Pruning, Quantization, Binarization, etc) can complement Federated Learning, enabling the efficient deployment of privacy-preserving models on resource-constrained edge devices.