Results 1 to 10 of about 72,357 (237)

FL-Incentivizer: FL-NFT and FL-Tokens for Federated Learning Model Trading and Training

IEEE Access, 2023
Federated learning (FL) is an on-device distributed learning scheme that does not require training devices to transfer their data to a centralized facility. The goal of federated learning is to learn a global model over several iterations.
Umer Majeed, Latif U. Khan, Sheikh Salman Hassan, Zhu Han, Choong Seon Hong   +4 more
doaj   +2 more sources

Adapting security and decentralized knowledge enhancement in federated learning using blockchain technology: literature review

Journal of Big Data
Federated Learning (FL) is a promising form of distributed machine learning that preserves privacy by training models locally without sharing raw data.
Menna Mamdouh Orabi, Osama Emam, Hanan Fahmy   +2 more
doaj   +2 more sources

Blockchain-Based Decentralized Federated Learning Method in Edge Computing Environment

Applied Sciences, 2023
In recent years, federated learning has been able to provide an effective solution for data privacy protection, so it has been widely used in financial, medical, and other fields.
Song Liu, Xiong Wang, Longshuo Hui, Weiguo Wu   +3 more
doaj   +1 more source

Decentralised Federated Learning for Hospital Networks With Application to COVID-19 Detection

IEEE Access, 2022
Federated Learning (FL) is a distributed machine learning technique which enables local learning of global machine learning models without the need of exchanging data.
Alessandro Giuseppi, Sabato Manfredi, Danilo Menegatti, Cecilia Poli, Antonio Pietrabissa   +4 more
doaj   +1 more source

The future of digital health with federated learning [PDF]

npj Digital Medicine, 2020
Data-driven machine learning (ML) has emerged as a promising approach for building accurate and robust statistical models from medical data, which is collected in huge volumes by modern healthcare systems.
Nicola Rieke, Jonny Hancox, Wenqi Li, F. Milletarì, H. Roth, Shadi Albarqouni, S. Bakas, M. Galtier, B. Landman, Klaus H. Maier-Hein, S. Ourselin, Micah J. Sheller, Ronald M. Summers, Andrew Trask, Daguang Xu, Maximilian Baust, M. Cardoso   +16 more
semanticscholar   +1 more source

Federated Learning on Non-IID Data Silos: An Experimental Study [PDF]

IEEE International Conference on Data Engineering, 2021
Due to the increasing privacy concerns and data regulations, training data have been increasingly fragmented, forming distributed databases of multiple “data silos” (e.g., within different organizations and countries).
Q. Li, Yiqun Diao, Quan Chen, Bingsheng He   +3 more
semanticscholar   +1 more source

FL-Defender: Combating Targeted Attacks in Federated Learning [PDF]

Knowledge-Based Systems, 2022
Federated learning (FL) enables learning a global machine learning model from local data distributed among a set of participating workers. This makes it possible i) to train more accurate models due to learning from rich joint training data, and ii) to ...
N. Jebreel, J. Domingo-Ferrer
semanticscholar   +1 more source

Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocation [PDF]

IEEE Transactions on Parallel and Distributed Systems, 2021
Federated learning (FL), as a distributed machine learning paradigm, promotes personal privacy by local data processing at each client. However, relying on a centralized server for model aggregation, standard FL is vulnerable to server malfunctions ...
Jun Li, Yumeng Shao, Kang Wei, Ming Ding, Chuan Ma, Long Shi, Zhu Han, Vincent Poor   +7 more
semanticscholar   +1 more source

Diabetic Retinopathy (DR) Detection and Grading Using Federated Learning (FL)

International Research Journal on Advanced Science Hub, 2023
Diabetic Retinopathy (DR) is the predominant and leading causes of blindness for people who have affected by diabetes in the world. DR Complication leads to affect the eyes and can lead to vision loss.
Priya Vishnu A S, Vijaykumar D, M. R, Suryaprakash P, S. R.   +4 more
semanticscholar   +1 more source

Federated Learning: A Distributed Shared Machine Learning Method

Complexity, 2021
Federated learning (FL) is a distributed machine learning (ML) framework. In FL, multiple clients collaborate to solve traditional distributed ML problems under the coordination of the central server without sharing their local private data with others ...
Kai Hu, Yaogen Li, Min Xia, Jiasheng Wu, Meixia Lu, Shuai Zhang, Liguo Weng   +6 more
doaj   +1 more source