Research map / Optimization / Distributed and compressed
Proximal and Federated Random Reshuffling
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Apply the proximal operator once per epoch instead of after every iteration | ProxRR vs. RR (heuristic) · Logistic regression with elastic-net regularization | convergence behavior | No clear effect |
| Apply the proximal operator once per epoch instead of after every iteration | ProxRR · Logistic regression with elastic-net regularization | proximal operator evaluations | Helped |
| Use FedRR rather than Local SGD | Federated training on homogeneous data · mushrooms | early convergence | Helped |
| Use FedRR rather than Local SGD | Federated training on homogeneous data · mushrooms | communication rounds and data passes | Helped |
| Reduce Local SGD's local steps to one fifth of a full data pass (ablation) | Local SGD · Federated training on homogeneous data | communication rounds and local steps | Helped |
Closest papers by meaning
- Don't Compress Gradients in Random Reshuffling: Compress Gradient Differences (NeurIPS 2024 · similarity 0.89)
- Rescaled Asynchronous SGD: Optimal Distributed Optimization under Data and System Heterogeneity (NeurIPS 2026 · similarity 0.88)
- Improved Last-Iterate Convergence of Shuffling Gradient Methods for Nonsmooth Convex Optimization (ICML 2025 · similarity 0.87)
- Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization (ICLR 2025 · similarity 0.87)
- Demystifying SGD with Doubly Stochastic Gradients (ICML 2024 · similarity 0.87)
- Stochastic Optimization with Arbitrary Recurrent Data Sampling (ICML 2024 · similarity 0.87)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.