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IoT sensors have already penetrated into extremely broad fields such as industrial production, smart home, environmental protection, medical diagnosis, and bioengineering. Although efficient data fusion helps improve the quality of intelligent services provided by the Internet of things, because the perceived data carry the sensitive information of the perceived object, the data fusion process is prone to the risk of privacy leakage. To this end, in this paper, we proposed a privacy-enhanced federated learning data fusion strategy. This strategy adds Gaussian noise at different stages of federated learning to achieve privacy protection in the data fusion process. Experimental results show that this strategy provides better privacy protection while achieving high-precision IoT data fusion.