Efficiency Analysis in Distributed Machine Learning
Analyzing the stochastic runtime of synchronous, asynchronous, and K-worker SGD, and discussing the time to reach target accuracy and …
distributed-machine-learning
Convergence Analysis in Federated Learning (Part 5)
Deriving non-convex stationary point bounds and strongly convex error bounds under full participation from FedAvg's local updates, …
Convergence Analysis in Distributed Machine Learning (Part 4)
Deriving strong convexity convergence bounds for synchronous, asynchronous, and K-Async SGD, clarifying assumptions of independent sampling, …
Convergence Analysis in Deep Learning (Part 3)
Deriving strong convex error bounds and non-convex stationary point bounds for mini-batch SGD under assumptions of conditionally unbiased …