Communication-Efficient Federated Bilevel Optimization with Local and Global Lower Level Problems
This work addresses communication bottlenecks in federated learning for bilevel optimization, which is incremental but improves efficiency for distributed machine learning tasks.
The authors tackled federated bilevel optimization by proposing FedBiOAcc, a communication-efficient algorithm that achieves O(ε^{-1}) communication complexity and O(ε^{-1.5}) sample complexity with linear speedup across clients.
Bilevel Optimization has witnessed notable progress recently with new emerging efficient algorithms. However, its application in the Federated Learning setting remains relatively underexplored, and the impact of Federated Learning's inherent challenges on the convergence of bilevel algorithms remain obscure. In this work, we investigate Federated Bilevel Optimization problems and propose a communication-efficient algorithm, named FedBiOAcc. The algorithm leverages an efficient estimation of the hyper-gradient in the distributed setting and utilizes the momentum-based variance-reduction acceleration. Remarkably, FedBiOAcc achieves a communication complexity $O(ε^{-1})$, a sample complexity $O(ε^{-1.5})$ and the linear speed up with respect to the number of clients. We also analyze a special case of the Federated Bilevel Optimization problems, where lower level problems are locally managed by clients. We prove that FedBiOAcc-Local, a modified version of FedBiOAcc, converges at the same rate for this type of problems. Finally, we validate the proposed algorithms through two real-world tasks: Federated Data-cleaning and Federated Hyper-representation Learning. Empirical results show superior performance of our algorithms.