Optimization Algorithms for Distributed Machine Learning

Discusses state-of-the-art algorithms that are at the core of the field of federated learning Analyzes each algorithm based on its error versus iterations convergence, and the runtime spent per iteration Provides insight into how the communication and synchronization protocol affects their practical performance