论文标题
关于联合双层优化问题的基于动量的算法的收敛
On the Convergence of Momentum-Based Algorithms for Federated Bilevel Optimization Problems
论文作者
论文摘要
在本文中，我们研究了联合的二重优化问题，该问题在机器学习中广泛应用。特别是，我们开发了两种基于动量的算法来优化此类问题，并确定了两种算法的收敛速率，提供了样本和通信复杂性。重要的是，据我们所知，我们的收敛速率是第一个实现了有关联合二元优化算法的设备数量的线性速度。最后，我们广泛的实验结果证实了我们两种算法的有效性。
In this paper, we studied the federated bilevel optimization problem, which has widespread applications in machine learning. In particular, we developed two momentum-based algorithms for optimizing this kind of problem and established the convergence rate of our two algorithms, providing the sample and communication complexities. Importantly, to the best of our knowledge, our convergence rate is the first one achieving the linear speedup with respect to the number of devices for federated bilevel optimization algorithms. At last, our extensive experimental results confirm the effectiveness of our two algorithms.
