<ul><li>Stochastic bilevel optimization (SBO) is increasingly important in machine learning and nested structures.</li><li>Decentralized approaches, like D-SOBA, improve communication efficiency and algorithmic robustness.</li><li>D-SOBA framework has two variants: D-SOBA-SO with second-order matrices, and D-SOBA-FO with first-order gradients.</li><li>Comprehensive non-asymptotic convergence analysis of D-SOBA reveals the impact of network topology and data heterogeneity.</li></ul>

Decentralized Bilevel Optimization: A Perspective from Transient Iteration Complexity

Discover more