The invention provides a
federated learning method for heterogeneous adaptive quantization
privacy protection. The
federated learning method comprises the following steps: S10, a
system initialization stage; s20, updating a local model by a user; s30, the
server calculates a quantization level; s40, calculating a quantitative gradient by the user; s50, protecting gradient privacy by the user; s60, the
server calculates Boolean sharing; s70, the
server converts Boolean sharing into arithmetic sharing; s80, the server calculates
global model parameters and auxiliary quantization levels and distributes the
global model parameters and the auxiliary quantization levels to all users; and S90, a model training stage: when the training round reaches the maximum value, terminating the federal learning task, and otherwise, entering the step S20. According to the
federated learning method for heterogeneous adaptive quantization
privacy protection, a
global model with relatively high accuracy can be obtained, the communication overhead and
privacy protection calculation overhead of participants are greatly reduced, and the federated learning method is very suitable for resource-limited lightweight
Internet of Things equipment in the intelligent
Internet of Things.