Secure Sharing and Protection of Agricultural Data Based on Federated Learning
Jiamin Zheng, Shijia Peng, Yipeng Zhou, Shaoyi Song, Xuan Zhao · 2025
With the acceleration of agricultural digitization process, the security sharing of agricultural data has become a key issue to be solved urgently. This paper focuses on this, introduces the related technologies such as blockchain, federated learning and interplanetary file system, and proposes the design of agricultural data sharing model based on federated learning. The work flow of this scheme is described in detail and evaluated by experiment. The security analysis shows that the scheme can effectively resist various security threats and protect the privacy of agricultural data. The accuracy analysis shows that the training accuracy of the model is almost unaffected while the safety is improved. Homomorphic encryption analysis verifies the effectiveness of the encryption technology in the scheme. This scheme provides a feasible and efficient solution for data security sharing of agricultural intelligent knowledge service platform, and is expected to play an important role in related fields.