The booming development of deep learning applications and services heavily relies on
large deep learning models and massive data in the cloud. However, cloud-based deep …

Federated learning is a new learning paradigm that decouples data collection and model
training via multi-party computation and model aggregation. As a flexible learning setting …

Federated learning (FL) typically faces data heterogeneity, ie, distribution shifting among
clients. Sharing clients' information has shown great potentiality in mitigating data …

With the flourish of digital technologies and rapid development of 5G and beyond networks,
Metaverse has become an increasingly hotly discussed topic, which offers users with …

Federated learning has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of federated learning, an …

Edge Intelligence (EI) allows Artificial Intelligence (AI) applications to run at the edge, where
data analysis and decision-making can be performed in real-time and close to data sources …

Evaluation is a systematic approach to assessing how well a system achieves its intended
purpose. Federated learning (FL) is a novel paradigm for privacy-preserving machine …

Recently, Heterogeneous Federated Learning (HtFL) has attracted attention due to its ability
to support heterogeneous models and data. To reduce the high communication cost of …

Federated learning (FL) is an efficient decentralized machine learning methodology for
processing nonindependent and identically distributed (non-IID) data due to geographical …

Abstract Heterogeneous Federated Learning (HtFL) enables collaborative learning on
multiple clients with different model architectures while preserving privacy. Despite recent …