IEEE

Federated Function-on-function Regression with an Efficient Gradient Boosting Algorithm for Privacy-Preserving Telemedicine

Abstract: Federated Learning (FL) is an emerging computing paradigm to collaboratively train Machine Learning (ML) models across multi-source data while preserving privacy. The major challenge of ...
IEEE

Treating the Filter Weights as Learnable Functions: An Efficient Nonlinear Filtering Framework and Its Adaptive Algorithms

Abstract: Adaptive filters, constrained by a linear filtering framework, often struggle with nonlinear modeling in complex processes. Kernel adaptive filters (KAFs) offer a promising solution by ...