Network Gradient Descent Algorithm for Decentralized Federated Learning
This work addresses privacy and reliability challenges in federated learning for distributed systems, though it appears incremental as it builds on existing gradient descent methods with network adaptations.
The authors tackled the problem of decentralized federated learning by proposing a network gradient descent (NGD) algorithm that operates without a central server, enhancing privacy and reliability. They found that with a small learning rate and balanced network structure, the NGD estimator can achieve statistical efficiency comparable to a global estimator, even with heterogeneous data.
We study a fully decentralized federated learning algorithm, which is a novel gradient descent algorithm executed on a communication-based network. For convenience, we refer to it as a network gradient descent (NGD) method. In the NGD method, only statistics (e.g., parameter estimates) need to be communicated, minimizing the risk of privacy. Meanwhile, different clients communicate with each other directly according to a carefully designed network structure without a central master. This greatly enhances the reliability of the entire algorithm. Those nice properties inspire us to carefully study the NGD method both theoretically and numerically. Theoretically, we start with a classical linear regression model. We find that both the learning rate and the network structure play significant roles in determining the NGD estimator's statistical efficiency. The resulting NGD estimator can be statistically as efficient as the global estimator, if the learning rate is sufficiently small and the network structure is well balanced, even if the data are distributed heterogeneously. Those interesting findings are then extended to general models and loss functions. Extensive numerical studies are presented to corroborate our theoretical findings. Classical deep learning models are also presented for illustration purpose.