In view of the large amount of new data in the Industrial Internet Of Things（IIOT） and the imbalance of data at the factory sub-ends， a data sharing method of IIOT based on Federal Incremental Learning （FIL-IIOT） was proposed. Firstly， the industry federation model was distributed to the factory sub-end as the local initial model. Then， the federal sub-end optimization algorithm was proposed to dynamically adjust the participating subset. Finally， the incremental weight of the factory sub-end was calculated through the federal incremental learning algorithm， thereby integrating the new state data with the original industry federation model quickly. Experimental results the Case Western Reserve University （CWRU） bearing failure dataset show that the proposed FIL-IIOT makes the accuracy of bearing fault diagnosis reached 93.15%， which is 6.18 percentage points and 2.59 percentage points higher than those of Federated Averaging （FedAvg） algorithm and FIL-IIOT of Non Increment （FIL-IIOT-NI） method， respectively. The proposed method meets the needs of continuous optimization of industry federation model based on industrial incremental data.