A comprehensive survey on privacy-preserving techniques in federated recommendation systems
Big data is a rapidly growing field, and new developments are constantly emerging to
address various challenges. One such development is the use of federated learning for …
address various challenges. One such development is the use of federated learning for …
Efficient privacy-preserving ML for IoT: Cluster-based split federated learning scheme for non-IID data
In this paper, we propose a solution to address the challenges of varying client resource
capabilities in the IoT environment when using the SplitFed architecture for training models …
capabilities in the IoT environment when using the SplitFed architecture for training models …
Lightweight Cross-Domain Authentication Scheme for Securing Wireless IoT Devices Using Backscatter Communication
Cross-domain collaboration under wireless communication scenarios has gained traction in
Internet of Things (IoT) applications. Authentication is essential for ensuring the security of …
Internet of Things (IoT) applications. Authentication is essential for ensuring the security of …
Towards cluster-based split federated learning approach for continuous user authentication
In today's rapidly evolving technological landscape, ensuring the security of systems
requires continuous authentication over sessions and comprehensive access management …
requires continuous authentication over sessions and comprehensive access management …
Enhancing Federated Learning Convergence with Dynamic Data Queue and Data Entropy-driven Participant Selection
Federated Learning (FL) is a decentralized approach for collaborative model training on
edge devices. This distributed method of model training offers advantages in privacy …
edge devices. This distributed method of model training offers advantages in privacy …
CRSFL: Cluster-based Resource-aware Split Federated Learning for Continuous Authentication
In the ever-changing world of technology, continuous authentication and comprehensive
access management are essential during user interactions with a device. Split Learning (SL) …
access management are essential during user interactions with a device. Split Learning (SL) …
Enhancing Mutual Trustworthiness in Federated Learning for Data-Rich Smart Cities
Federated learning is a promising collaborative and privacy-preserving machine learning
approach in data-rich smart cities. Nevertheless, the inherent heterogeneity of these urban …
approach in data-rich smart cities. Nevertheless, the inherent heterogeneity of these urban …
Towards Mutual Trust-Based Matching For Federated Learning Client Selection
Federated Learning (FL) is a revolutionary privacy-preserving distributed learning
framework that allows a small group of users to cooperatively build a machine-learning …
framework that allows a small group of users to cooperatively build a machine-learning …
DLShield: A Defense Approach Against Dirty Label Attacks in Heterogeneous Federated Learning
KM Sameera, M Abhinav, PP Amal, TB Abhiram… - … Conference on Security …, 2024 - Springer
Federated Learning (FL) is a privacy-focused revolutionary approach distributed paradigm
that supports considerable devices to train a shared model collaboratively without …
that supports considerable devices to train a shared model collaboratively without …
Enhancing Client Privacy in Physiology-Based Biometric Verification with Differential Privacy and Positive-Label Federated Learning
In recent years, the widespread adoption of multimodal physiological signal-based biometric
systems has led to a significant increase in data exchange on cloud servers. This surge in …
systems has led to a significant increase in data exchange on cloud servers. This surge in …