Method and system for federated learning

Abstract

Methods, systems, and apparatuses, including computer programs encoded on computer storage media, for federal learning with differentially private (DP) intrinsic quantization are disclosed. One exemplary method may include obtaining a parameter vector of a local model; updating the parameter vector of the local model by adding a plurality of noise vectors to the parameter vector of the local model; performing quantization to the updated parameter vector to obtain a quantized parameter vector, wherein the quantization maps coordinates in the updated parameter vector to a set of discrete finite values; and transmitting, to a server, the quantized parameter vector and at least one of the plurality of noise vectors for the server to update a global model.