Results 131 to 140 of about 72,357 (237)

ns3-fl: Simulating Federated Learning with ns-3

Proceedings of the 2022 Workshop on ns-3, 2022
Emily Ekaireb, Xiaofan Yu, Kazim Ergun, Quanling Zhao, Kai Lee, Muhammad Huzaifa, Tajana Rosing   +6 more
openaire   +1 more source

Federated Learning in Dynamic and Heterogeneous Environments: Advantages, Performances, and Privacy Problems

Applied Sciences
Federated Learning (FL) represents a promising distributed learning methodology particularly suitable for dynamic and heterogeneous environments characterized by the presence of Internet of Things (IoT) devices and Edge Computing infrastructures. In this
Fabio Liberti, Davide Berardi, Barbara Martini   +2 more
doaj   +1 more source

EneA-FL: Energy-aware orchestration for serverless federated learning

Future Generation Computer Systems
Federated Learning (FL) represents the de-facto standard paradigm for enabling distributed learning over multiple clients in real-world scenarios. Despite the great strides reached in terms of accuracy and privacy awareness, the real adoption of FL in real-world scenarios, in particular in industrial deployment environments, is still an open thread ...
Andrea Agiollo, Paolo Bellavista, Matteo Mendula, Andrea Omicini   +3 more
openaire   +2 more sources

Enhanced federated learning for secure medical data collaboration

Journal of Analytical Science and Technology
Federated learning (FL) enables collaborative model training across multiple institutions while preserving data privacy. However, conventional encryption techniques used in FL remain vulnerable to quantum attacks, raising concerns about the security of ...
Benjamin Appiah, Isaac Osei, Bill K. Frimpong, Daniel Commey, Kwabena Owusu-Agymang, Gabriel Assamah   +5 more
doaj   +1 more source

Micro-FL: A Fault-Tolerant Scalable Microservice-Based Platform for Federated Learning

Future Internet
As the number of machine learning applications increases, growing concerns about data privacy expose the limitations of traditional cloud-based machine learning methods that rely on centralized data collection and processing.
Mikael Sabuhi, Petr Musilek, Cor-Paul Bezemer   +2 more
doaj   +1 more source

Decision Trees in Federated Learning: Current State and Future Opportunities

IEEE Access
Federated learning (FL) is a distributed machine learning technique that enables multiple decentralized clients to develop a model collaboratively without exchanging their local data.
Sudath R. Heiyanthuduwage, Irfan Altas, Michael Bewong, Md Zahidul Islam, Oscar B. Deho   +4 more
doaj   +1 more source

AWDP-FL: An Adaptive Differential Privacy Federated Learning Framework

Electronics
Data security and user privacy concerns are increasingly gaining attention. Federated learning models based on differential privacy offer a distributed machine learning framework that protects data privacy; however, the added noise can impact the model's utility, making performance evaluation crucial.
Zhiyan Chen, Hong Zheng, Gang Liu
openaire   +1 more source

Recent advances on federated learning systems and the design for computing power Internet of things

物联网学报
Computing power Internet of things (CPIoT) integrates Internet of things (IoT) devices with substantial computational resources to support data-intensive tasks, facilitating intelligent decision-making.
LU Jianfeng, QI Pan, PAN Linyu, LI Bing, CAO Shuqin, JIN Yan'an   +5 more
doaj  

Federated intelligence for smart grids: a comprehensive review of security and privacy strategies

Journal of Electrical Systems and Information Technology
The increasing complexity and interconnectivity of smart grid (SG) systems have exposed them to a wide array of cybersecurity threats. This review paper critically surveys recent advancements in federated learning (FL) as a privacy-preserving machine ...
Raseel Z. Alshamasi, Dina M. Ibrahim
doaj   +1 more source

Meta-FL: A Novel Meta-Learning Framework for Optimizing Heterogeneous Model Aggregation in Federated Learning

Federated Learning (FL) enables collaborative model training across diverse entities while safeguarding data privacy. However, FL faces challenges such as data heterogeneity and model diversity.
Alsulaimawi, Zahir
core