Defending Against Data Reconstruction Attacks in Federated Learning: An Information Theory Approach
Explore advanced defense strategies against data reconstruction attacks in federated learning through information theory, focusing on privacy preservation and efficient training methods.
Lotto: Secure Participant Selection against Adversarial Servers in Federated Learning
Explore secure participant selection in Federated Learning through Lotto, a system designed to protect against adversarial servers while maintaining efficient performance and privacy guarantees.
Decentralized Federated Machine Learning: Empowering Edge Devices with Kubernetes
Discover how to implement Federated Machine Learning with Kubernetes for privacy-preserving model training across edge devices, enabling efficient image classification and scaled computations.
User-Level and Federated Local Differential Privacy in Statistical Inference
Explore fundamental statistical inference challenges in distributed data systems with differential privacy constraints across servers and users, focusing on privacy-preserving information transmission.
FAMOS: Robust Privacy-Preserving Authentication on Payment Apps via Federated Multi-Modal Contrastive Learning
Discover how FAMOS framework enhances mobile payment security through federated multi-modal contrastive learning, offering robust user authentication while protecting privacy and handling background noise.