Abstract: The specification provides a method and a system for collaboratively updating a model by multiple parties for implementing privacy protection. A server can deliver an aggregation result of a t-th round of common samples to each participant i. Each participant i performs first update on a local ith model according to the t-th round of common samples and the aggregation result. Each participant i performs second update on the ith model obtained after the first update based on a first private sample fixed in a local sample set and a sample label thereof. Each participant i inputs a (t+1)th round of common samples that are used for a next round of iteration into the ith model obtained after the second update, and sends an output second prediction result to the server, so the server aggregates n second prediction results corresponding to n participants for a next round of iteration.

Abstract: This specification disclose methods, apparatus, and systems for updating machine learning models. In one implementation, a method includes: determining, by a participant of a plurality of participants, a local gradient vector based on a local sample set and current model parameters, obtaining a perturbed gradient vector by performing random binarization processing on the local gradient vector based on a differential privacy algorithm, and sending the perturbed gradient vector to a server. The method further includes receiving a target gradient vector from the server. The target gradient vector is determined by performing binary representation on an aggregation result of aggregating a plurality of perturbed gradient vectors received from the plurality of participants. The method further includes updating, by the participant, the current model parameters based on the target gradient vector.