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 …
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 is an emerging distributed machine learning framework for privacy preservation. However, models trained in federated learning usually have worse …
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 …
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 …
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 …
Heterogeneity across clients in federated learning (FL) usually hinders the optimization convergence and generalization performance when the aggregation of clients' knowledge …
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 …
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 …