Results 41 to 50 of about 129,870 (304)

Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning [PDF]

, 2020
Federated learning is a distributed framework for training machine learning models over the data residing at mobile devices, while protecting the privacy of individual users.
Avestimehr, A. Salman, Guler, Basak, So, Jinhyun   +2 more
core   +1 more source

Toward Quantum Federated Learning

IEEE Transactions on Neural Networks and Learning Systems
Quantum Federated Learning (QFL) is an emerging interdisciplinary field that merges the principles of Quantum Computing (QC) and Federated Learning (FL), with the goal of leveraging quantum technologies to enhance privacy, security, and efficiency in the learning process.
Chao Ren, Rudai Yan, Huihui Zhu, Han Yu, Minrui Xu, Yuan Shen, Yan Xu, Ming Xiao, Zhao Yang Dong, Mikael Skoglund, Dusit Niyato, Leong Chuan Kwek   +11 more
openaire   +3 more sources

Towards Personalized Federated Learning

IEEE Transactions on Neural Networks and Learning Systems, 2023
Accepted by IEEE Transactions on Neural Networks and Learning ...
Alysa Ziying Tan, Han Yu, Lizhen Cui, Qiang Yang   +3 more
openaire   +4 more sources

A review of federated learning in renewable energy applications: Potential, challenges, and future directions

Energy and AI
Federated learning has recently emerged as a privacy-preserving distributed machine learning approach. Federated learning enables collaborative training of multiple clients and entire fleets without sharing the involved training datasets.
Albin Grataloup, Stefan Jonas, Angela Meyer   +2 more
doaj   +1 more source

Federated meta learning: a review

大数据, 2023
With the popularity of mobile devices, massive amounts of data are constantly produced.The data privacy policies are becoming more and more specified, the flow and use of data are strictly regulated.Federated learning can break data barriers and use ...
Chuanyao ZHANG, Shijing SI, Jianzong WANG, Jing XIAO   +3 more
doaj  

Survey of Federated Incremental Learning [PDF]

Jisuanji kexue
Federated learning,with its unique distributed training mode and secure aggregation mechanism,has become a research hotspot in recent years.However,in real-life scenarios,local model training often faces new data,leading to catastrophic forgetting of old
XIE Jiachen, LIU Bo, LIN Weiwei , ZHENG Jianwen
doaj   +1 more source

New Generation Federated Learning

Sensors, 2022
With the development of the Internet of things (IoT), federated learning (FL) has received increasing attention as a distributed machine learning (ML) framework that does not require data exchange. However, current FL frameworks follow an idealized setup in which the task size is fixed and the storage space is unlimited, which is impossible in the real
Boyuan Li, Shengbo Chen, Zihao Peng
openaire   +3 more sources

Research review of federated learning algorithms

大数据, 2020
In recent years,federated learning has been proposed and received widespread attention to overcome data isolated island challenge.Federated learning related researches were adopted in areas such as financial field,healthcare domain and smart city related
Jianzong WANG, Lingwei KONG, Zhangcheng HUANG, Linjie CHEN, Yi LIU, Anxun HE, Jing XIAO   +6 more
doaj  

A Review of Research on Secure Aggregation for Federated Learning

Future Internet
Federated learning (FL) is an advanced distributed machine learning method that effectively solves the data silo problem. With the increasing popularity of federated learning and the growing importance of privacy protection, federated learning methods ...
Xing Zhang, Yuexiang Luo, Tianning Li
doaj   +1 more source

Federated Learning Via Inexact ADMM

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
One of the crucial issues in federated learning is how to develop efficient optimization algorithms. Most of the current ones require full device participation and/or impose strong assumptions for convergence. Different from the widely-used gradient descent-based algorithms, in this paper, we develop an inexact alternating direction method of ...
Shenglong Zhou, Geoffrey Ye Li
openaire   +4 more sources