Results 41 to 50 of about 6,308,360 (314)

Privatized Graph Federated Learning

EURASIP Journal on Advances in Signal Processing, 2023
Abstract Federated learning is a semi-distributed algorithm, where a server communicates with multiple dispersed clients to learn a global model. The federated architecture is not robust and is sensitive to communication and computational overloads due to its one-master multi-client structure. It can also be subject to privacy attacks targeting
Elsa Rizk, Stefan Vlaski, Ali H. Sayed
openaire   +7 more sources

Blockchain-Based Federated Learning: A Survey and New Perspectives

Applied Sciences
Federated learning, as a novel distributed machine learning mode, enables the training of machine learning models on multiple devices while ensuring data privacy.
Weiguang Ning, Yingjuan Zhu, Caixia Song, Hongxia Li, Lihui Zhu, Jinbao Xie, Tianyu Chen, Tong Xu, Xi Xu, Jiwei Gao   +9 more
doaj   +1 more source

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

Learning From Others Without Sacrificing Privacy: Simulation Comparing Centralized and Federated Machine Learning on Mobile Health Data

JMIR mHealth and uHealth, 2021
BackgroundThe use of wearables facilitates data collection at a previously unobtainable scale, enabling the construction of complex predictive models with the potential to improve health.
Liu, Jessica Chia, Goetz, Jack, Sen, Srijan, Tewari, Ambuj   +3 more
doaj   +1 more source

Revolutionizing healthcare with federated reinforcement learning: from machine learning to machine unlearning [PDF]

, 2023
The landscape of healthcare is undergoing a transformative shift with the emergence of artificial intelligence (AI) and machine learning (ML) technologies, particularly in remote patient monitoring systems. These systems offer real-time data on patients’
Shaik, Thanveer Basha
core   +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  

Federated Residual Learning

CoRR, 2020
We study a new form of federated learning where the clients train personalized local models and make predictions jointly with the server-side shared model. Using this new federated learning framework, the complexity of the central shared model can be minimized while still gaining all the performance benefits that joint training provides.
Alekh Agarwal, John Langford 0001, Chen-Yu Wei   +2 more
openaire   +2 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  

Digital Cognitive Phenotyping for Differential Diagnosis and Monitoring in Neurological Conditions

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To assess the utility, accessibility, and equivalence to supervised scales of online cognitive assessment in older individuals with cognitive impairment. Methods Patients with Alzheimer's disease (AD, n = 31), idiopathic normal pressure hydrocephalus (iNPH, n = 26), and traumatic brain injury (TBI, n = 23) completed online cognitive ...
Martina Del Giovane, Valentina Giunchiglia, Michael C. B. David, Magdalena A. Kolanko, William R. Trender, Peter J. Hellyer, Harmeena Kaur, David J. Sharp, Christopher Carswell, Paresh A. Malhotra, Adam Hampshire   +10 more
wiley   +1 more source

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