GPVO-FL: Grouped Privacy-Preserving and Verification-Outsourced Federated Learning in Cloud-Edge Collaborative Environment
Shiwen Zhang, Feixiang Ren, Wei Liang, Kuan‐Ching Li, Nam Ling · IEEE Transactions on Network and Service Management · 2025
As a form of distributed machine learning, Federated learning allows users to complete training without sharing local data, thereby protecting user privacy to a certain extent. However, the gradients uploaded by users during the training process can still leak user privacy. Additionally, malicious or lazy cloud servers may tamper with or forge the aggregated results before returning them to users, causing significant losses to the entire training process. Existing solutions focus on security issues, but most privacy protection schemes based on complex cryptographic primitives require high computational power and communication bandwidth. Moreover, to verify the aggregated results, each user must compute proofs, which imposes an additional computational burden on users. Therefore, designing more efficient and lightweight solutions that ensure security while adapting to resource-constrained scenarios is necessary. An efficient group-based scheme for privacy preservation and verification outsourcing in federated learning, referred to as GPVO-FL, is introduced in this work. Specifically, we design a lightweight privacy protection mechanism based on group structure and masking techniques to protect user gradients. In addition, we design an outsourced verification mechanism that offloads the verification process to edge servers, thus reducing the computational burden on users. A detailed security and experimental analysis demonstrates the security and efficiency of our scheme.