Results 71 to 80 of about 17,879 (167)
Secure and decentralized federated learning framework with non-IID data based on blockchain
HeliyonFederated learning enables the collaborative training of machine learning models across multiple organizations, eliminating the need for sharing sensitive data.
Feng Zhang +3 more
doaj +1 more source
Survey on federated recommendation systems
大数据, 2022 In the federated learning (FL) paradigm, the original data are stored in independent clients while masked data are sent to a central server to be aggregated, which proposes a novel design approach to numerous domains.Given the wide application of ...
Zhitao ZHU +3 more
doaj
Massive MIMO for Serving Federated Learning and Non-Federated Learning Users
IEEE Transactions on Wireless CommunicationsWith its privacy preservation and communication efficiency, federated learning (FL) has emerged as a promising learning framework for beyond 5G wireless networks. It is anticipated that future wireless networks will jointly serve both FL and downlink non-FL user groups in the same time-frequency resource.
Muhammad Farooq 0002 +3 more
openaire +4 more sources
Detecting Electrocardiogram Arrhythmia Empowered With Weighted Federated Learning
IEEE AccessIn this study, a weighted federated learning approach is proposed for electrocardiogram (ECG) arrhythmia classification. The proposed approach considers the heterogeneity of data distribution among multiple clients in federated learning settings.
Rizwana Naz Asif +6 more
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Federated learning (FL) represents a significant advancement in distributed machine learning, enabling multiple participants to collaboratively train models without sharing raw data. This decentralized approach enhances privacy by keeping data on local devices.
Jaydip Sen, Hetvi Waghela, Sneha Rakshit
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CCM-FL: Covert communication mechanisms for federated learning in crowd sensing IoT
Digital Communications and NetworksThe past decades have witnessed a wide application of federated learning in crowd sensing, to handle the numerous data collected by the sensors and provide the users with precise and customized services.
Hongruo Zhang +4 more
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Transformer-Based Federated Learning Models for Recommendation Systems
IEEE AccessIn today’s data-driven environment, safeguarding user privacy is a top priority, particularly in machine learning applications. Our study introduces an innovative approach that combines the privacy-preserving attributes of federated learning with ...
M. Sujaykumar Reddy +2 more
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SemFedXAI: A Semantic Framework for Explainable Federated Learning in Healthcare
InformationFederated Learning (FL) is emerging as an encouraging paradigm for AI model training in healthcare that enables collaboration among institutions without revealing sensitive information.
Alba Amato, Dario Branco
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Precision‐Weighted Federated Learning
Computational IntelligenceABSTRACT Federated learning (FL) using the federated averaging (FedAvg) algorithm has shown great advantages for large‐scale applications that rely on collaborative learning, especially when the training data is either unbalanced or inaccessible due to privacy constraints. We hypothesize that FedAvg underestimates
Jonatan Reyes +3 more
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Secure Federated Learning Algorithms for Vertical and Combined Data Partitioning
Future InternetWith the growing need for collaborative machine learning across institutions holding sensitive data, ensuring data privacy without compromising model performance has become an important challenge. This work introduces secure federated learning algorithms
Amir Anees +3 more
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