A full dive into realizing the edge-enabled metaverse: Visions, enabling technologies, and challenges

M Xu, WC Ng, WYB Lim, J Kang, Z Xiong… - … Surveys & Tutorials, 2022 - ieeexplore.ieee.org
Dubbed “the successor to the mobile Internet,” the concept of the Metaverse has grown in
popularity. While there exist lite versions of the Metaverse today, they are still far from …

Heterogeneous federated learning: State-of-the-art and research challenges

M Ye, X Fang, B Du, PC Yuen, D Tao - ACM Computing Surveys, 2023 - dl.acm.org
Federated learning (FL) has drawn increasing attention owing to its potential use in large-
scale industrial applications. Existing FL works mainly focus on model homogeneous …

Blockchain-empowered federated learning: Challenges, solutions, and future directions

J Zhu, J Cao, D Saxena, S Jiang, H Ferradi - ACM Computing Surveys, 2023 - dl.acm.org
Federated learning is a privacy-preserving machine learning technique that trains models
across multiple devices holding local data samples without exchanging them. There are …

Federated learning on non-IID data: A survey

H Zhu, J Xu, S Liu, Y Jin - Neurocomputing, 2021 - Elsevier
Federated learning is an emerging distributed machine learning framework for privacy
preservation. However, models trained in federated learning usually have worse …

Towards personalized federated learning

AZ Tan, H Yu, L Cui, Q Yang - IEEE transactions on neural …, 2022 - ieeexplore.ieee.org
In parallel with the rapid adoption of artificial intelligence (AI) empowered by advances in AI
research, there has been growing awareness and concerns of data privacy. Recent …

Exploiting shared representations for personalized federated learning

L Collins, H Hassani, A Mokhtari… - … on machine learning, 2021 - proceedings.mlr.press
Deep neural networks have shown the ability to extract universal feature representations
from data such as images and text that have been useful for a variety of learning tasks …

Layer-wised model aggregation for personalized federated learning

X Ma, J Zhang, S Guo, W Xu - Proceedings of the IEEE/CVF …, 2022 - openaccess.thecvf.com
Abstract Personalized Federated Learning (pFL) not only can capture the common priors
from broad range of distributed data, but also support customized models for heterogeneous …

Fedproto: Federated prototype learning across heterogeneous clients

Y Tan, G Long, L Liu, T Zhou, Q Lu, J Jiang… - Proceedings of the …, 2022 - ojs.aaai.org
Heterogeneity across clients in federated learning (FL) usually hinders the optimization
convergence and generalization performance when the aggregation of clients' knowledge …

A survey on security and privacy of federated learning

V Mothukuri, RM Parizi, S Pouriyeh, Y Huang… - Future Generation …, 2021 - Elsevier
Federated learning (FL) is a new breed of Artificial Intelligence (AI) that builds upon
decentralized data and training that brings learning to the edge or directly on-device. FL is a …

No fear of heterogeneity: Classifier calibration for federated learning with non-iid data

M Luo, F Chen, D Hu, Y Zhang… - Advances in Neural …, 2021 - proceedings.neurips.cc
A central challenge in training classification models in the real-world federated system is
learning with non-IID data. To cope with this, most of the existing works involve enforcing …