Distributed Optimization, Learning, and Inference

I read this because distributed machine learning is not just about hardware and throughput.

The optimization side matters just as much:

• communication constraints
• consensus and coordination
• convergence behavior under decentralization
• how system design choices influence learning dynamics

It fits well with my interest in large-scale training and distributed ML.