Distributed kernel training improves robust gradient learning rates
Generalization Analysis of Distributed Kernel-based Robust Gradient Descent Algorithms
Machine Learning
Summary
Training machine learning models across multiple computers can be tricky, especially when the data has noise or errors. This paper studies a method that uses special math functions called kernels combined with a strong way to handle errors, making the training more reliable. The authors found a smarter way to set parameters so the learning is both fast and accurate. They also developed a new technique that cuts down the communication needed between computers, speeding up the whole learning process.
What this means in practice
- •For machine learning engineers: Improve training speed and accuracy of distributed kernel-based models in noisy data environments using robust gradient descent with optimized parameters.
- •For data center operators: Reduce communication overhead in distributed learning systems by implementing the proposed communication-efficient strategy for kernel-based gradient algorithms.
Authors
Jun-Yi Meng, Zheng-Chu Guo, Yuan Mao
Abstract
In this paper, we investigate the generalization performance of distributed gradient descent algorithms in a reproducing kernel Hilbert space under a robust loss function $l_σ$. By exploiting the spectral characterization of gradient descent together with the intrinsic properties of robust loss functions, we establish optimal learning rates for the distributed kernel-based robust gradient descent (DKRGD) algorithm with an appropriately chosen scale parameter $σ$. The proposed parameter choice of $σ$ simultaneously alleviates the saturation phenomenon and guarantees statistical robustness. A key technical contribution is a novel error analysis that provides substantially sharper bounds for products of operators, thereby significantly relaxing existing restrictions on the maximum number of local machines while retaining optimal learning rates. Finally, we develop a communication-efficient strategy that further improves the convergence performance of DKRGD.