A novel single-loop algorithm, SLDBO, is proposed for efficiently solving decentralized bilevel optimization problems without any heterogeneity assumptions, and it achieves the best-known convergence rate.
This paper introduces DAGM, a novel decentralized algorithm for bilevel optimization that leverages a penalty function and decentralized Hessian inverse approximation to achieve efficient communication and fast convergence.
This paper introduces C2DFB, a novel decentralized bilevel optimization algorithm that achieves both communication and computation efficiency by leveraging first-order gradient oracles and a reference point-based compression strategy.