5 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
Multi-modal knowledge graph completion (MMKGC) aims to infer missing entities of triples by leveraging heterogeneous information in knowledge graph (KG). However, existing approaches often struggle with inconsistent modality alignment, limited reasoning depth, and insufficient negative sample qualit
Questionnaire data serve as a valuable resource across numerous scientific domains, offering insights into human behavior, health, and social trends. Traditional downsampling-based representation learning methods—such as standardization and one-hot encoding—reformat these data into tabular structure
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
Carefully selecting clients to participate in aggregation can assist the global model in achieving better performance. However, existing research on federated heterogeneous graph learning (FHGL) has shown limited attention to the client selection (CS) problem. Current CS algorithms face challenges i…