Results 201 to 210 of about 72,357 (237)

Transitioning From Federated Learning to Quantum Federated Learning in Internet of Things: A Comprehensive Survey

IEEE Communications Surveys and Tutorials
Quantum Federated Learning (QFL) recently becomes a promising approach with the potential to revolutionize Machine Learning (ML). It merges the established strengths of classical Federated Learning (FL) with the exceptional parallel mechanism and ...
Cheng Qiao, Mianjie Li, Yuan Liu, Z. Tian   +3 more
semanticscholar   +1 more source

Democratizing Federated Learning: “FL-Insight” as an Interactive Visual Demonstrator for SME Adoption

2025 3rd International Conference on Federated Learning Technologies and Applications (FLTA)
Despite the potential of federated learning (FL) to enable privacy-preserving collaboration, small and mediumsized enterprises (SMEs) face significant barriers to adoption, including technical complexity and difficulty visualizing tangible benefits.
Thomas Van Den Bossche, Laurens Van De Perre, Abasin Abdul Khalil   +2 more
semanticscholar   +1 more source

Federated Learning for IoT: A Survey of Techniques, Challenges, and Applications

J. Sens. Actuator Networks
Federated Learning (FL) has emerged as a pivotal approach for decentralized Machine Learning (ML), addressing the unique demands of the Internet of Things (IoT) environments where data privacy, bandwidth constraints, and device heterogeneity are ...
Elias Dritsas, Maria Trigka
semanticscholar   +1 more source

A Credible and Fair Federated Learning Framework Based on Blockchain

IEEE Transactions on Artificial Intelligence
Federated learning (FL) enables cooperative computation between multiple participants while protecting user privacy. Currently, FL algorithms assume that all participants are trustworthy and their systems are secure. However, the following problems arise
Leiming Chen, Dehai Zhao, Liping Tao, Kai Wang, Sibo Qiao, Xingjie Zeng, C. Tan   +6 more
semanticscholar   +1 more source

Secure Federated Learning With Fully Homomorphic Encryption for IoT Communications

IEEE Internet of Things Journal
The emergence of the Internet of Things (IoT) has revolutionized people’s daily lives, providing superior quality services in cognitive cities, healthcare, and smart buildings. However, smart buildings use heterogeneous networks.
Neveen Mohammad Hijazi, Moayad Aloqaily, M. Guizani, B. Ouni, F. Karray   +4 more
semanticscholar   +1 more source

Virtual Contrast-Enhanced MRI Synthesis with High Model Generalizability Using Trusted Federated Learning (FL-TrustVCE): A Multi-institutional Study

CMMCA@MICCAI, 2023
Wen Li, Yiming Shi, Saikit Lam, A. Cheung, Haonan Xiao, Chenyang Liu, Tian Li, Shaohua Zhi, Bernie Liu, F. Lee, Kwok-hung Au, V. Lee, Jing Cai   +12 more
semanticscholar   +1 more source

Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration

Healthcare
Federated learning (FL) is revolutionizing healthcare by enabling collaborative machine learning across institutions while preserving patient privacy and meeting regulatory standards. This review delves into FL’s applications within smart health systems,
Syed Raza Abbas, Zeeshan Abbas, Arifa Zahir, S. Lee   +3 more
semanticscholar   +1 more source

SPinS-FL: Communication-Efficient Federated Subnetwork Learning

2023 IEEE 20th Consumer Communications & Networking Conference (CCNC), 2023
Masayoshi Tsutsui, Shinya Takamaeda-Yamazaki   +1 more
openaire   +1 more source

Improving LoRA in Privacy-preserving Federated Learning

International Conference on Learning Representations
Low-rank adaptation (LoRA) is one of the most popular task-specific parameter-efficient fine-tuning (PEFT) methods on pre-trained language models for its good performance and computational efficiency.
Youbang Sun, Zitao Li, Yaliang Li, Bolin Ding   +3 more
semanticscholar   +1 more source

Clustered Federated Learning With Adaptive Local Differential Privacy on Heterogeneous IoT Data

IEEE Internet of Things Journal
The Internet of Things (IoT) is penetrating many aspects of our daily life with the proliferation of artificial intelligence applications. Federated learning (FL) has emerged as a promising paradigm enabling many intelligent IoT applications; however ...
Zaobo He, Lintao Wang, Zhipeng Cai
semanticscholar   +1 more source