A Lightweight and Accuracy-Lossless Privacy-Preserving Method in Federated Learning

The emergence of big data and artificial intelligence (AI) marks a significant milestone in technological advancement, impacting various sectors and transforming the essence of public and personal activities. Traditional machine learning, known as centralized learning, involves collecting data from multiple sources for the model development, which poses significant privacy risks. To address the conflict between the need for data sharing and the protection of privacy, federated learning (FL) has emerged as a promising solution. This approach allows for the continuous sharing of model updates between a central server and numerous local devices, fostering collaborative model development. However, it also brings about considerable communication costs and raises privacy concerns due to the potential for sensitive information leakage from the central server and local devices. To address these issues, we propose a lightweight and accuracy-lossless privacy-preserving FL scheme based on gradient clipping. The central server introduces noise to the global model to prevent clients from extracting valuable information, and then the local devices train these models using their data. To reduce the communication load, parameters with less impact on the model are removed using the Fisher information matrix. Additionally, to enhance privacy protection, the client-computed parameters are perturbed using the Diffie-Hellman key exchange method. Our experiments show that this approach greatly reduces the communication load for clients and the server, while effectively safeguarding client privacy and maintaining the model’s accuracy.