3 papers
cs.LG2025
Heterogeneous Federated Learning Systems for Time-Series Power Consumption Prediction with Multi-Head Embedding Mechanism
Jia-Hao Syu, Jerry Chun-Wei Lin, Gautam Srivastava +1
Time-series prediction is increasingly popular in a variety of applications, such as smart factories and smart transportation. Researchers have used various techniques to predict p…
cs.LG2025
Distributed Multi-Head Learning Systems for Power Consumption Prediction
Jia-Hao Syu, Jerry Chun-Wei Lin, Philip S. Yu
As more and more automatic vehicles, power consumption prediction becomes a vital issue for task scheduling and energy management. Most research focuses on automatic vehicles in tr…
cs.LG2025
Heterogeneous Federated Learning System for Sparse Healthcare Time-Series Prediction
Jia-Hao Syu, Jerry Chun-Wei Lin
In this paper, we propose a heterogeneous federated learning (HFL) system for sparse time series prediction in healthcare, which is a decentralized federated learning algorithm wit…