Distributed machine learning

As machine learning moves away from data-center architectures to learning with edge devices (like smartphones, IoT sensors etc.), new paradigms such as federated learning are emerging. Here data resides on edge devices (not sent to a central server), and a collaborative model using distributed devices is built through interactive communication. This necessitates efficient use of limited communication bandwidth, privacy guarantees for user data and security against malicious adversaries. We are working on all these fronts as described below. Please see

• Personalized Federated Learning
• Communication efficient distributed machine learning
• Privacy in distributed machine learning 
• Robust and secure distributed machine learning 
• Encoded distributed optimization