Reviewing federated machine learning and its use in diseases prediction

M Moshawrab, M Adda, A Bouzouane, H Ibrahim… - Sensors, 2023 - mdpi.com
Machine learning (ML) has succeeded in improving our daily routines by enabling
automation and improved decision making in a variety of industries such as healthcare …

The significance of machine learning in clinical disease diagnosis: A review

SM Rahman, S Ibtisum, E Bazgir, T Barai - arXiv preprint arXiv:2310.16978, 2023 - arxiv.org
The global need for effective disease diagnosis remains substantial, given the complexities
of various disease mechanisms and diverse patient symptoms. To tackle these challenges …

Machine and deep learning for iot security and privacy: applications, challenges, and future directions

S Bharati, P Podder - Security and communication networks, 2022 - Wiley Online Library
The integration of the Internet of Things (IoT) connects a number of intelligent devices with
minimum human interference that can interact with one another. IoT is rapidly emerging in …

Exploring privacy measurement in federated learning

GK Jagarlamudi, A Yazdinejad, RM Parizi… - The Journal of …, 2024 - Springer
Federated learning (FL) is a collaborative artificial intelligence (AI) approach that enables
distributed training of AI models without data sharing, thereby promoting privacy by design …

Comparative analysis of open-source federated learning frameworks-a literature-based survey and review

P Riedel, L Schick, R von Schwerin, M Reichert… - International Journal of …, 2024 - Springer
Abstract While Federated Learning (FL) provides a privacy-preserving approach to analyze
sensitive data without centralizing training data, the field lacks an detailed comparison of …

Federated learning with deep convolutional neural networks for the detection of multiple chest diseases using chest x-rays

H Malik, T Anees - Multimedia Tools and Applications, 2024 - Springer
The increasing global incidence of COVID-19 necessitates the rapid development of a
reliable method for diagnosing the disease. The virus is spreading so quickly, that medical …

Transparency and privacy: the role of explainable ai and federated learning in financial fraud detection

T Awosika, RM Shukla, B Pranggono - IEEE Access, 2024 - ieeexplore.ieee.org
Fraudulent transactions and how to detect them remain a significant problem for financial
institutions around the world. The need for advanced fraud detection systems to safeguard …

[HTML][HTML] A survey of security strategies in federated learning: Defending models, data, and privacy

HU Manzoor, A Shabbir, A Chen, D Flynn, A Zoha - Future Internet, 2024 - mdpi.com
Federated Learning (FL) has emerged as a transformative paradigm in machine learning,
enabling decentralized model training across multiple devices while preserving data …

The role of llms in sustainable smart cities: Applications, challenges, and future directions

A Ullah, G Qi, S Hussain, I Ullah, Z Ali - arXiv preprint arXiv:2402.14596, 2024 - arxiv.org
Smart cities stand as pivotal components in the ongoing pursuit of elevating urban living
standards, facilitating the rapid expansion of urban areas while efficiently managing …

Federated learning based privacy ensured sensor communication in IoT networks: a taxonomy, threats and attacks

SI Manzoor, S Jain, Y Singh, H Singh - Ieee Access, 2023 - ieeexplore.ieee.org
Our daily lives are significantly impacted by intelligent Internet of Things (IoT) application,
services, IoT gadgets, and more intelligent industries. Artificial Intelligence (AI) is anticipated …