Results 61 to 70 of about 17,541 (167)

FedADC: Federated Average Knowledge Distilled Mutual Conditional Learning (FedADC) for Waste Classification

IEEE Access
Federated learning presents a potent avenue for addressing challenges in waste classification, where diverse datasets are distributed across sources.
Ananya Ghosh, Parthiban Krishnamoorthy
doaj   +1 more source

Chained Anomaly Detection Models for Federated Learning: An Intrusion Detection Case Study

Applied Sciences, 2018
The adoption of machine learning and deep learning is on the rise in the cybersecurity domain where these AI methods help strengthen traditional system monitoring and threat detection solutions.
Davy Preuveneers, Vera Rimmer, Ilias Tsingenopoulos, Jan Spooren, Wouter Joosen, Elisabeth Ilie-Zudor   +5 more
doaj   +1 more source

Federated Learning-Based Framework: A New Paradigm Proposed for Supply Chain Risk Management

Engineering Proceedings
This paper proposes federated learning-based frameworks for supply chain risk management to address data-sharing constraints. To validate, centralized federated learning with horizontal data was applied for delivery delay prediction using datasets from ...
Thanh Tuan Nguyen, Abdelghani Bekrar, Thi Muoi Le, Abdelhakim Artiba, Tarik Chargui, Thi Thu Huong Trinh, Ahmed Snoun   +6 more
doaj   +1 more source

Feature-Based Federated Transfer Learning: Communication Efficiency, Robustness and Privacy

IEEE Transactions on Machine Learning in Communications and Networking
In this paper, we propose feature-based federated transfer learning as a novel approach to improve communication efficiency by reducing the uplink payload by multiple orders of magnitude compared to that of existing approaches in federated learning and ...
Feng Wang, M. Cenk Gursoy, Senem Velipasalar   +2 more
doaj   +1 more source

Challenges and Countermeasures of Federated Learning Data Poisoning Attack Situation Prediction

Mathematics
Federated learning is a distributed learning method used to solve data silos and privacy protection in machine learning, aiming to train global models together via multiple clients without sharing data.
Jianping Wu, Jiahe Jin, Chunming Wu
doaj   +1 more source

Federated Versus Central Machine Learning on Diabetic Foot Ulcer Images: Comparative Simulations

IEEE Access
This research examines the implementation of the U-Net model within a federated learning framework, focusing on the semantic segmentation of Diabetic Foot Ulcers (DFUs) images.
Mahdi Saeedi, Hamed Taheri Gorji, Fartash Vasefi, Kouhyar Tavakolian   +3 more
doaj   +1 more source

Survey on incentive-driven federated learning: privacy and security

网络与信息安全学报
Federated learning was enabled to allow multiple data holders to jointly complete machine learning tasks without disclosing local data. Incentivizing participants to engage in federated learning and contribute high-quality data was identified as one of ...
CHI Huanhuan, XIONG Ping, LIU Hengzhu, MA Xiao, ZHU Tianqing   +4 more
doaj  

Toward Quantum Federated Learning

IEEE Transactions on Neural Networks and Learning Systems
Quantum Federated Learning (QFL) is an emerging interdisciplinary field that merges the principles of Quantum Computing (QC) and Federated Learning (FL), with the goal of leveraging quantum technologies to enhance privacy, security, and efficiency in the learning process.
Chao Ren 0006, Rudai Yan, Huihui Zhu, Han Yu 0001, Minrui Xu, Yuan Shen, Yan Xu 0005, Ming Xiao 0001, Zhao Yang Dong, Mikael Skoglund, Dusit Niyato, Leong Chuan Kwek   +11 more
openaire   +3 more sources

Federated Learning in Data Privacy and Security

Advances in Distributed Computing and Artificial Intelligence Journal
Federated learning (FL) has been a rapidly growing topic in recent years. The biggest concern in federated learning is data privacy and cybersecurity. There are many algorithms that federated models have to work on to achieve greater efficiency, security,
Dokuru Trisha Reddy, Haripriya Nandigam, Sai Charan Indla, S. P. Raja   +3 more
doaj   +1 more source

Secure and decentralized federated learning framework with non-IID data based on blockchain

Heliyon
Federated learning enables the collaborative training of machine learning models across multiple organizations, eliminating the need for sharing sensitive data.
Feng Zhang, Yongjing Zhang, Shan Ji, Zhaoyang Han   +3 more
doaj   +1 more source