 Customers first collect data from various IoT devices using phones, and then  download and train the initial model with their data. During the training, customers first extract features using their mobiles, and then add the Laplacian noise to the extracted features based on differential privacy, a formal and popular notion to quantify privacy. After achieving the local model , customers sign on their models respectively and send them to the blockchain.
<clarity>  Customers first collect data from various IoT devices using phones, and then  download and train the initial model with their data. During the training, customers first extract features using their mobiles, and then add the Laplacian noise to the extracted features based on differential privacy, a formal and popular notion to quantify privacy. After achieving the local model , customers sign on their models respectively and send them to the blockchain.
 Customers first collect data from various IoT devices using phones, and then download and train the initial model with their local data. After deriving local models , customers sign on their models respectively and send them to the blockchain.