Federated learning‐based colorectal cancer classification by convolutional neural networks and general visual representation learning
Sign Up to like & getrecommendations! Published in 2023 at "International Journal of Imaging Systems and Technology"
DOI: 10.1002/ima.22875
Abstract: Colorectal cancer is the fourth fatal disease in the world, and the massive burden on the pathologists related to the classification of precancerous and cancerous colorectal lesions can be decreased by deep learning (DL) methods.… read more here.
Keywords: visual representation; representation learning; federated learning; learning ... See more keywords
CRFL: A novel federated learning scheme of client reputation assessment via local model inversion
Sign Up to like & getrecommendations! Published in 2022 at "International Journal of Intelligent Systems"
DOI: 10.1002/int.22914
Abstract: Federated learning (FL) is gradually becoming a key learning paradigm in Privacy‐preserving Machine Learning (ML) systems. In FL, a large number of clients cooperate with a central server to learn a shared model without sharing… read more here.
Keywords: client; federated learning; crfl; model ... See more keywords
Security of federated learning for cloud‐edge intelligence collaborative computing
Sign Up to like & getrecommendations! Published in 2022 at "International Journal of Intelligent Systems"
DOI: 10.1002/int.22992
Abstract: Federated Learning (FL) is one of the key technologies to solve privacy protection for cloud‐edge intelligent collaborative computing, and its security and privacy issues have attracted extensive attention from academia and industry. FL is a… read more here.
Keywords: collaborative computing; federated learning; cloud edge; privacy ... See more keywords
Robust privacy‐preserving federated learning framework for IoT devices
Sign Up to like & getrecommendations! Published in 2022 at "International Journal of Intelligent Systems"
DOI: 10.1002/int.22993
Abstract: Federated Learning (FL) is a framework where multiple parties can train a model jointly without sharing private data. Private information protection is a critical problem in FL. However, the communication overheads of existing solutions are… read more here.
Keywords: federated learning; iot devices; framework; privacy preserving ... See more keywords
Federated learning with stochastic quantization
Sign Up to like & getrecommendations! Published in 2022 at "International Journal of Intelligent Systems"
DOI: 10.1002/int.23056
Abstract: This paper studies the distributed federated learning problem when the exchanged information between the server and the workers is quantized. A novel quantized federated averaging algorithm is developed by applying stochastic quantization scheme to the… read more here.
Keywords: model parameters; stochastic quantization; federated learning; model ... See more keywords
A survey on federated learning in data mining
Sign Up to like & getrecommendations! Published in 2022 at "Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery"
DOI: 10.1002/widm.1443
Abstract: Data mining is a process to extract unknown, hidden, and potentially useful information from data. But the problem of data island makes it arduous for people to collect and analyze scattered data, and there is… read more here.
Keywords: learning data; mining; data mining; survey federated ... See more keywords
FedMEC: Improving Efficiency of Differentially Private Federated Learning via Mobile Edge Computing
Sign Up to like & getrecommendations! Published in 2020 at "Mobile Networks and Applications"
DOI: 10.1007/s11036-020-01586-4
Abstract: Federated learning is a recently proposed paradigm that presents significant advantages in privacy-preserving machine learning services. It enables the deep learning applications on mobile devices, where a deep neural network (DNN) is trained in a… read more here.
Keywords: federated learning; edge computing; differentially private; edge ... See more keywords
Federated learning of predictive models from federated Electronic Health Records
Sign Up to like & getrecommendations! Published in 2018 at "International journal of medical informatics"
DOI: 10.1016/j.ijmedinf.2018.01.007
Abstract: BACKGROUND In an era of "big data," computationally efficient and privacy-aware solutions for large-scale machine learning problems become crucial, especially in the healthcare domain, where large amounts of data are stored in different locations and… read more here.
Keywords: federated learning; electronic health; learning predictive; predictive models ... See more keywords
Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Results
Sign Up to like & getrecommendations! Published in 2020 at "Medical image analysis"
DOI: 10.1016/j.media.2020.101765
Abstract: Deep learning models have shown their advantage in many different tasks, including neuroimage analysis. However, to effectively train a high-quality deep learning model, the aggregation of a significant amount of patient information is required. The… read more here.
Keywords: multi site; federated learning; analysis; model ... See more keywords
Applications of federated learning in smart cities: recent advances, taxonomy, and open challenges
Sign Up to like & getrecommendations! Published in 2021 at "Connection Science"
DOI: 10.1080/09540091.2021.1936455
Abstract: Federated learning plays an important role in the process of smart cities. With the development of big data and artificial intelligence, there is a problem of data privacy protection in this process. Federated learning is… read more here.
Keywords: learning smart; cities recent; federated learning; applications federated ... See more keywords
A dynamic global backbone updating for communication-efficient personalised federated learning
Sign Up to like & getrecommendations! Published in 2022 at "Connection Science"
DOI: 10.1080/09540091.2022.2114428
Abstract: ABSTRACT Federated learning (FL) is an emerging distributed machine learning technique. However, when dealing with heterogeneous data, a shared global model cannot generalise all devices' local data. Furthermore, the FL training process necessitates frequent parameter… read more here.
Keywords: communication efficient; efficient personalised; communication; federated learning ... See more keywords