The invention discloses a semantic communication privacy enhanced federal multitask learning method for
mobile edge computing. The method comprises the following steps: initializing a
global model comprising a shared
encoder and a plurality of personalized decoders by an
edge server, and issuing the
global model; after each mobile edge
client performs multi-task training locally, performing semantic compression
processing of integrated error compensation, sparsification, quantification and coding on model updating, performing
discretization mapping on features by using a local private
codebook to enhance privacy, and uploading compressed data; and after the
server side receives the data, inferring a nonlinear
relation graph among global tasks through
adaptive kernel learning, and according to the nonlinear
relation graph, performing differential weighted aggregation of structure
perception on updating execution of the shared
encoder and each personalized decoder, updating a
global model, and issuing the updated global model to the
client side for next iteration. According to the method, the endogenous
privacy protection is realized while the communication overhead is reduced, and the efficiency, safety and performance of federal multitask learning in a resource-limited mobile edge environment are effectively improved.