A method for training a model, the method comprising: receiving, by a service device with a trusted zone, encrypted data sent by user equipment; decrypting, by using a decryption algorithm that can be executed only in the trusted zone, the encrypted data in the trusted zone to obtain user data; and training (S 108) a model by using the user data in the trusted zone; wherein the trusted zone is located in a CPU of the service device, characterised in that: the service device comprises multiple first CPUs and at least one second CPU; receiving encrypted data sent by user equipment comprises receiving encrypted data sent by multiple user equipment; decrypting the encrypted data in the trusted zone to obtain user data comprises decrypting the encrypted data in trusted zones of first CPUs to obtain the user data, wherein the encrypted data from different user equipment is decrypted in trusted zones of different first CPUs; and training a model by using the user data in the trusted zone comprises: training a model in a trusted zone of each first CPU based on user data obtained through decryption in the first CPU and a model parameter sent by the second CPU, obtaining a training intermediate value, and outputting the training intermediate value to the second CPU by using the first CPU; determining a training effect representative value by using the second CPU based on the training intermediate values output by the first CPUs; and adjusting the model parameter based on the training effect representative value, so that the second CPU and each first CPU continue to train a model based on an adjusted model parameter until a trained model satisfies a specified condition. receiving, by a service device with a trusted zone, encrypted data sent by user equipment; decrypting, by using a decryption algorithm that can be executed only in the trusted zone, the encrypted data in the trusted zone to obtain user data; and training (S 108) a model by using the user data in the trusted zone; wherein the trusted zone is located in a CPU of the service device, characterised in that: the service device comprises multiple first CPUs and at least one second CPU; receiving encrypted data sent by user equipment comprises receiving encrypted data sent by multiple user equipment; decrypting the encrypted data in the trusted zone to obtain user data comprises decrypting the encrypted data in trusted zones of first CPUs to obtain the user data, wherein the encrypted data from different user equipment is decrypted in trusted zones of different first CPUs; and training a model by using the user data in the trusted zone comprises: training a model in a trusted zone of each first CPU based on user data obtained through decryption in the first CPU and a model parameter sent by the second CPU, obtaining a training intermediate value, and outputting the training intermediate value to the second CPU by using the first CPU; determining a training effect representative value by using the second CPU based on the training intermediate values output by the first CPUs; and adjusting the model parameter based on the training effect representative value, so that the second CPU and each first CPU continue to train a model based on an adjusted model parameter until a trained model satisfies a specified condition. the service device comprises multiple first CPUs and at least one second CPU; receiving encrypted data sent by user equipment comprises receiving encrypted data sent by multiple user equipment; decrypting the encrypted data in the trusted zone to obtain user data comprises decrypting the encrypted data in trusted zones of first CPUs to obtain the user data, wherein the encrypted data from different user equipment is decrypted in trusted zones of different first CPUs; and training a model by using the user data in the trusted zone comprises: training a model in a trusted zone of each first CPU based on user data obtained through decryption in the first CPU and a model parameter sent by the second CPU, obtaining a training intermediate value, and outputting the training intermediate value to the second CPU by using the first CPU; determining a training effect representative value by using the second CPU based on the training intermediate values output by the first CPUs; and adjusting the model parameter based on the training effect representative value, so that the second CPU and each first CPU continue to train a model based on an adjusted model parameter until a trained model satisfies a specified condition. training a model in a trusted zone of each first CPU based on user data obtained through decryption in the first CPU and a model parameter sent by the second CPU, obtaining a training intermediate value, and outputting the training intermediate value to the second CPU by using the first CPU; determining a training effect representative value by using the second CPU based on the training intermediate values output by the first CPUs; and adjusting the model parameter based on the training effect representative value, so that the second CPU and each first CPU continue to train a model based on an adjusted model parameter until a trained model satisfies a specified condition.