3 accepted papers
Split Federated Learning (SFL) has emerged as a pivotal paradigm for privacy-preserving distributed training on resource-constrained edge devices by partitioning neural networks between clients and a server. A critical design choice in SFL is the split layer, which determines the computation distrib
Spatial-temporal prediction plays a crucial role in various domains, including intelligent transportation and environmental monitoring. Although large language model has shown advantages in long-range dependency modeling and excellent generalization ability for forecasting, it has limited understand
Federated Learning (FL) enables collaborative model training across distributed devices without sharing raw data. Hierarchical Federated Learning (HFL) is a new paradigm of FL that leverages the Edge Servers (ESs) layer as an intermediary to perform partial local model aggregation in proximity, redu