Federated Learning(FL)is a new computing paradigm in privacy-preserving Machine Learning(ML),where the ML model is trained in a decentralized manner by the clients,preventing the server from directly accessing privacy...Federated Learning(FL)is a new computing paradigm in privacy-preserving Machine Learning(ML),where the ML model is trained in a decentralized manner by the clients,preventing the server from directly accessing privacy-sensitive data from the clients.Unfortunately,recent advances have shown potential risks for user-level privacy breaches under the cross-silo FL framework.In this paper,we propose addressing the issue by using a three-plane framework to secure the cross-silo FL,taking advantage of the Local Differential Privacy(LDP)mechanism.The key insight here is that LDP can provide strong data privacy protection while still retaining user data statistics to preserve its high utility.Experimental results on three real-world datasets demonstrate the effectiveness of our framework.展开更多
基金supported by the National Key R&D Program of China under Grant 2020YFB1806904by the National Natural Science Foundation of China under Grants 61872416,62171189,62172438 and 62071192+1 种基金by the Fundamental Research Funds for the Central Universities of China under Grant 2019kfyXJJS017,31732111303,31512111310by the special fund for Wuhan Yellow Crane Talents(Excellent Young Scholar).
文摘Federated Learning(FL)is a new computing paradigm in privacy-preserving Machine Learning(ML),where the ML model is trained in a decentralized manner by the clients,preventing the server from directly accessing privacy-sensitive data from the clients.Unfortunately,recent advances have shown potential risks for user-level privacy breaches under the cross-silo FL framework.In this paper,we propose addressing the issue by using a three-plane framework to secure the cross-silo FL,taking advantage of the Local Differential Privacy(LDP)mechanism.The key insight here is that LDP can provide strong data privacy protection while still retaining user data statistics to preserve its high utility.Experimental results on three real-world datasets demonstrate the effectiveness of our framework.
