FLIBD: A federated learning-based IoT big data management approach for privacy-preserving over Apache Spark with FATE
A Karras, A Giannaros, L Theodorakopoulos… - Electronics, 2023 - mdpi.com
In this study, we introduce FLIBD, a novel strategy for managing Internet of Things (IoT) Big
Data, intricately designed to ensure privacy preservation across extensive system networks …
Data, intricately designed to ensure privacy preservation across extensive system networks …
Privacy-enhancing and robust backdoor defense for federated learning on heterogeneous data
Federated learning (FL) allows multiple clients to train deep learning models collaboratively
while protecting sensitive local datasets. However, FL has been highly susceptible to …
while protecting sensitive local datasets. However, FL has been highly susceptible to …
Secure and privacy-preserving federated learning with explainable artificial intelligence for smart healthcare system
A Raza - 2023 - theses.hal.science
The growing population around the globe has a significant impact on various sectors
including the labor force, healthcare, and the global economy. The healthcare sector is …
including the labor force, healthcare, and the global economy. The healthcare sector is …
Fedward: Flexible federated backdoor defense framework with non-IID data
Z Chen, F Wang, Z Zheng, X Liu… - 2023 IEEE International …, 2023 - ieeexplore.ieee.org
Federated learning (FL) enables multiple clients to collaboratively train deep learning
models while considering sensitive local datasets' privacy. However, adversaries can …
models while considering sensitive local datasets' privacy. However, adversaries can …
Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications
Adversarial attacks such as poisoning attacks have attracted the attention of many machine
learning researchers. Traditionally, poisoning attacks attempt to inject adversarial training …
learning researchers. Traditionally, poisoning attacks attempt to inject adversarial training …