Review for NeurIPS paper: Throughput-Optimal Topology Design for Cross-Silo Federated Learning

The paper proposes methods for designing communication graph for the decentralized periodic averaging SGD (DPASGD) in the federated learning set up focusing on reducing the per-iteration complexity (cycle time). The reviews were very appreciative of the good system and experimental design aspects of the paper that accounts for various types of delays in realistic scenarios. I would like to thank the authors for their effort. The reviewers were quite engaged and have provided many useful feedback and I hope these will be used to improve the paper. In particular, I would like to comment of few points -- please see full reviews for details - Although the authors motivate the need for focusing on cycle time over convergence rate in the introduction, based on the reviews, I believe it would be useful to include this discussion explicitly as a highlighted paragraph or subsection (see also comments by R2 on digraph constraint) - I would also encourage you to consider the title change suggestion by R2 (or something similar) as I and other reviewers agree that the current title is too generic.