Federated Smoothing Proximal Gradient for Quantile Regression with Non-Convex Penalties
This work addresses data privacy and efficiency issues in IoT analytics by enabling federated quantile regression with nonconvex penalties, though it appears incremental as it builds on existing federated and smoothing techniques.
The paper tackles the challenge of performing quantile regression on high-dimensional sparse data from distributed IoT sensors while keeping data on-device, proposing a federated smoothing proximal gradient (FSPG) algorithm that integrates smoothing with proximal gradients to handle nonconvex penalties, resulting in improved estimation precision and reliable convergence as validated by simulations.
Distributed sensors in the internet-of-things (IoT) generate vast amounts of sparse data. Analyzing this high-dimensional data and identifying relevant predictors pose substantial challenges, especially when data is preferred to remain on the device where it was collected for reasons such as data integrity, communication bandwidth, and privacy. This paper introduces a federated quantile regression algorithm to address these challenges. Quantile regression provides a more comprehensive view of the relationship between variables than mean regression models. However, traditional approaches face difficulties when dealing with nonconvex sparse penalties and the inherent non-smoothness of the loss function. For this purpose, we propose a federated smoothing proximal gradient (FSPG) algorithm that integrates a smoothing mechanism with the proximal gradient framework, thereby enhancing both precision and computational speed. This integration adeptly handles optimization over a network of devices, each holding local data samples, making it particularly effective in federated learning scenarios. The FSPG algorithm ensures steady progress and reliable convergence in each iteration by maintaining or reducing the value of the objective function. By leveraging nonconvex penalties, such as the minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD), the proposed method can identify and preserve key predictors within sparse models. Comprehensive simulations validate the robust theoretical foundations of the proposed algorithm and demonstrate improved estimation precision and reliable convergence.