Smoothed Gradient Method for Nonconvex Federated Stochastic Bilevel Optimization
cs.LG
Submitted: 2026-10-04
Updated: 2026-10-04
Sources
- Federated Meta-Learning with Fast Convergence and Efficient Communication
- Tighter Analysis of Alternating Stochastic Gradient Method for Stochastic Nested Problems
- On the Convergence of Momentum-Based Algorithms for Federated Bilevel Optimization Problems
- Approximation Methods for Bilevel Programming
- Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification
- Fast Adaptive Federated Bilevel Optimization
- Improving Federated Learning Personalization via Model Agnostic Meta Learning
Related papers
- Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
- AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
- Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
- Convergence issues in Relational Concept Analysis based on AOC-posets
- Beliefs Beyond Posteriors: The Predictive Cost of ELBO Binding
- Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks