Research map / Federated learning and privacy / Communication-efficient FL
Solving a Class of Non-Convex Minimax Optimization in Federated Learning
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.
Closest papers by meaning
- SAGDA: Achieving $\mathcal{O}(\epsilon^{-2})$ Communication Complexity in Federated Min-Max Learning (NeurIPS 2022 · similarity 0.93)
- Zeroth-Order Methods for Nondifferentiable, Nonconvex, and Hierarchical Federated Optimization (NeurIPS 2023 · similarity 0.91)
- A Communication-efficient Algorithm with Linear Convergence for Federated Minimax Learning (NeurIPS 2022 · similarity 0.91)
- FedSMU: Communication-Efficient and Generalization-Enhanced Federated Learning through Symbolic Model Updates (ICML 2025 · similarity 0.88)
- Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization (ICLR 2025 · similarity 0.88)
- SILVER: Single-loop variance reduction and application to federated learning (ICML 2024 · similarity 0.88)
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.
