In this paper, we studied the federated stochastic bilevel optimization problem. In particular, we developed two momentum-based algorithms for optimizing this kind of problem. In addition, we established the convergence rate of these two algorithms, providing their sample and communication complexities. To the best of our knowledge, this is the first work achieving such favorable theoretical results.

翻译：在本文中,我们研究了联合的随机双级优化问题。特别是,我们开发了两种基于动力的算法来优化这类问题。此外,我们建立了这两种算法的趋同率,提供了它们的样本和通信复杂性。据我们所知,这是首次取得这种有利的理论结果。