uMK-HEFL: Unconstrained multi-key homomorphic encryption for privacy-preserving federated learning

This publication has 20 references indexed in Scilit:

• MVFL: verifiable privacy-preserving federated learning using multi-key homomorphic encryption
  The Journal of Supercomputing, 2025
• Pivacy-preserving federated learning based on multi-key fully homomorphic encryption and trusted execution environment
  Peer-to-Peer Networking and Applications, 2025
• A multi-center federated learning mechanism based on consortium blockchain for data secure sharing
  Knowledge-Based Systems, 2025
• Privacy-Preserving Federal Learning Chain for Internet of Things
  IEEE Internet of Things Journal, 2023
• A federated learning scheme meets dynamic differential privacy
  CAAI Transactions on Intelligence Technology, 2023
• An adaptive federated learning scheme with differential privacy preserving
  Future Generation Computer Systems, 2022
• Privacy‐preserving federated learning based on multi‐key homomorphic encryption
  International Journal of Intelligent Systems, 2022
• Local Differential Privacy-Based Federated Learning for Internet of Things
  IEEE Internet of Things Journal, 2020
• Privacy-Preserving Federated Learning Framework Based on Chained Secure Multiparty Computing
  IEEE Internet of Things Journal, 2020
• Incentive Design and Differential Privacy Based Federated Learning: A Mechanism Design Perspective
  IEEE Access, 2020