Mar 18, 2024 · In this paper, we propose a set of novel Federated Learning Schemes by utilizing the latest homomorphic encryption technologies, so as to ...

Mar 18, 2024 · The experiment results show that our scheme achieves significant improvements in security, efficiency and practicality, compared with classical ...

Multi-Key Fully Homomorphic Encryption (MKFHE) is a promising technique that allows computations on ciphertexts encrypted by different parties. MKFHE's aptitude ...

In this paper, we propose an efficient and privacy-preserving federated deep learning protocol based on stochastic gradient descent method by integrating the ...

Federated learning (FL) technology has emerged for efficient data collection, data privacy protection, and efficient utilization of computing resources.

This paper proposes a privacy-preserving federated learning algorithm for medical data using homomorphic encryption that uses a secure multi-party ...

This method effectively reduces communication overhead and integrates seamlessly with homomorphic encryption schemes. • Based on the assumption of a trusted ...

Mar 10, 2024 · MULTI-KEY FULLY HOMOMORPHIC ENCRYPTION FOR PRIVACY-PRESERVATION WITHIN FEDERATED LEARNING ENVIRONMENTS ... In this paper, we revisit fully ...

Based on blockchain, drivers are authenticated with zero-knowledge proof. They used homomorphic encryption in their traceable identity-based approach because ...

Oct 27, 2023 · Abstract: Federated Learning trains machine learning models on distributed devices by aggregating local model updates instead of local data.