Security of federated learning in 6G era: A review on conceptual techniques and software platforms used for research and analysis
Federated Learning (FL) is an emerging Artificial Intelligence (AI) paradigm enabling
multiple parties to train a model collaboratively without sharing their data. With the upcoming …
multiple parties to train a model collaboratively without sharing their data. With the upcoming …
Advancing Federated Learning: Optimizing Model Accuracy through Privacy-Conscious Data Sharing
Our innovative federated learning approach addresses the evolving landscape of
collaborative machine learning by strategically sharing 80\% of the dataset among …
collaborative machine learning by strategically sharing 80\% of the dataset among …
[HTML][HTML] Enabling efficient and low-effort decentralized federated learning with the EdgeFL framework
Abstract Context: Federated Learning (FL) has gained prominence as a solution for
preserving data privacy in machine learning applications. However, existing FL frameworks …
preserving data privacy in machine learning applications. However, existing FL frameworks …
Biomedical data classification using fuzzy clustering
One of the computer-aided technologies that is growing at a very fast speed is medicine.
Lots of research has already been done in which the nature of medical data was studied …
Lots of research has already been done in which the nature of medical data was studied …
Federated Learning for Decentralized DDoS Attack Detection in IoT Networks
In the ever-expanding domain of Internet of Things (IoT) networks, Distributed Denial of
Service (DDoS) attacks represent a significant challenge, compromising the reliability of …
Service (DDoS) attacks represent a significant challenge, compromising the reliability of …
[HTML][HTML] Evaluating Federated Learning Simulators: A Comparative Analysis of Horizontal and Vertical Approaches
Federated learning (FL) is a decentralized machine learning approach whereby each device
is allowed to train local models, eliminating the requirement for centralized data collecting …
is allowed to train local models, eliminating the requirement for centralized data collecting …
The Integration of Federated Learning Techniques in Predictive Aircraft Maintenance Using Cloud Services
K Tigchelaar, SS Mohammadi Ziabari… - Principle and Practice of …, 2024 - Springer
Federated Learning (FL) has emerged as a key research topic, providing a decentralized
approach to model training while preserving data privacy. This research focuses on the …
approach to model training while preserving data privacy. This research focuses on the …
Securing Federated Learning in IoT: A Survey of Attacks, Defenses, and Frameworks
Federated Learning (FL) is a powerful Machine Learning (ML) technique that allows multiple
clients to collaborate on training models while kee** their data private. Unlike traditional …
clients to collaborate on training models while kee** their data private. Unlike traditional …
Balancing Privacy Preservation and Accuracyoptimization in Federated Learning for E-Health
In the rapidly evolving field of modern artificial intelligence, where collaborative machine
learning plays a crucial role, the increasingimportance of privacy preservation in …
learning plays a crucial role, the increasingimportance of privacy preservation in …
Analisis perbandingan pengaruh variasi data augmentasi terhadap kinerja mobilenetv2 dalam klasifikasi penyakit daun teh
M Farhan - repository.uinjkt.ac.id
Penelitian ini bertujuan untuk menganalisis perbandingan kinerja MobileNetV2 dengan 32
Variasi data augmentasi yang berbeda dalam klasifikasi penyakit daun teh. Penyakit daun …
Variasi data augmentasi yang berbeda dalam klasifikasi penyakit daun teh. Penyakit daun …