HDHRFL: A hierarchical robust federated learning framework for dual-heterogeneous and noisy clients
Federated learning (FL) is a distributed machine learning approach in which many clients
contribute to learning a single global model in a privacy-preserving manner on the server …
contribute to learning a single global model in a privacy-preserving manner on the server …
[PDF][PDF] A Systematic Survey on Federated Semi-supervised Learning
Federated learning (FL) revolutionizes distributed machine learning by enabling devices to
collaboratively learn a model while maintaining data privacy. However, FL usually faces a …
collaboratively learn a model while maintaining data privacy. However, FL usually faces a …
Reinforcement Learning-Based Personalized Differentially Private Federated Learning
Due to the different privacy and local model quality requirements for each participant,
federated learning (FL) is vulnerable to membership inference attacks. To solve this issue …
federated learning (FL) is vulnerable to membership inference attacks. To solve this issue …
Cycle-Fed: A Double-Confidence Unlabeled Data Augmentation Method Based on Semisupervised Federated Learning
The Internet of Things (IoT) generates a substantial volume of unlabeled personal privacy
data in finance and healthcare, distributed across diverse locations and networks, which is …
data in finance and healthcare, distributed across diverse locations and networks, which is …
MAFS: Modality-Aware Federated Semi-Supervised Learning with Selective Data Sharing Specified by Individual Clients
Compared to unimodal data, multimodal sensor data improves model performance for
complex tasks. Federated Learning (FL) further enhances this by preserving data privacy …
complex tasks. Federated Learning (FL) further enhances this by preserving data privacy …