Optimal Convergence Rates for Convex Distributed Optimization in Networks

By Kevin Scaman, Francis R. Bach, Kilian Q. Weinberger. 2019

90 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Yoshua Bengio
2. Behnam Neyshabur
3. Jianyu Wang
4. Sergey Levine
5. Dacheng Tao
6. Ruslan Salakhutdinov
7. Nicolas Ballas
8. Hanie Sedghi

Citations in the corpus

• Robust Aggregation for Federated Learning (2019, methodology): “[57] K.”
• SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum (2019, background): “Scaman et al. (2019) establish optimal rates of convergence for decentralized optimization methods in the deterministic, convex setting.”
• The Role of Permutation Invariance in Linear Mode Connectivity of Neural Networks (2021, methodology): “Linear mode connectivity between solutions or checkpoints also allows for weight averaging techniques for distributed optimization to be used as effectively in deep learning as convex optimization (Scaman et al., 2019).”