As information technology advances， the collection， processing， analysis and application of agricultural data have become the primary driving force of smart agriculture. In traditional smart agricultural management systems， it is usually required to centralize agricultural data on a central server for analysis and model training， which often poses the risk of data leakage. The leakage of key agricultural privacy data seriously affects the interests of farmers and agricultural institutions， so many the farmers and institutions will carefully handle the issue of sharing original data. To address this issue， federated learning allows different agricultural institutions， farms and agricultural enterprises to complete the training of farming decision models under the condition of only sharing encrypted models， reducing the risk of agricultural privacy data leakage and protecting the legitimate rights and interests of data providers. The theoretical development， technological innovation and practical application of federated learning technology in the field of smart agriculture were introduced. Based on the development trend of smart agriculture systems， it proposed design suggestions for a smart agriculture system based on federated learning. This paper provided references for researchers and practitioners in related fields， offering theoretical value and practical guidance for advancing agricultural data science， ensuring agricultural data security and enhancing the level of agricultural intelligence.
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