Differentially Private Federated Learning via Inexact ADMM
Differential privacy (DP) techniques can be applied to the federated learning model to protect data privacy against inference attacks to communication among the learning agents. The DP techniques, however, hinder achieving a greater learning performance while ensuring strong data privacy. In this paper we develop a DP inexact alternating direction method of multipliers algorithm that solves a sequence of subproblems with the objective perturbation by random noises generated from a Laplace distribution. We show that our algorithm provides $\bar{\epsilon}$-DP for every iteration, where $\bar{\epsilon}$ is a privacy parameter controlled by a user. Using MNIST and FEMNIST datasets for the image classification, we demonstrate that our algorithm reduces the testing error by at most $22\%$ compared with the existing DP algorithm, while achieving the same level of data privacy. The numerical experiment also shows that our algorithm converges faster than the existing algorithm.
Code (0)
등록된 구현이 없습니다.
Tasks
Federated Learningimage-classificationImage ClassificationSimilar Papers 제목 키워드 기반
Differentially Private Federated Learning via Inexact ADMM with Multiple Local Updates
Differential privacy (DP) techniques can be applied to the federated learning model to statistically guarantee data privacy against inference attacks to communication among the learning agents. While ensuring strong data…
Federated Learningimage-classificationImage ClassificationFrom Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning
We study differentially private (DP) machine learning algorithms as instances of noisy fixed-point iterations, in order to derive privacy and utility results from this well-studied framework. We show that this new perspe…
Federated LearningDifferentially Private ADMM for Distributed Medical Machine Learning
Due to massive amounts of data distributed across multiple locations, distributed machine learning has attracted a lot of research interests. Alternating Direction Method of Multipliers (ADMM) is a powerful method of des…
BIG-bench Machine LearningFedADMM-InSa: An Inexact and Self-Adaptive ADMM for Federated Learning
Federated learning (FL) is a promising framework for learning from distributed data while maintaining privacy. The development of efficient FL algorithms encounters various challenges, including heterogeneous data and sy…
Federated LearningCommunication-Efficient ADMM-based Federated Learning
Federated learning has shown its advances over the last few years but is facing many challenges, such as how algorithms save communication resources, how they reduce computational costs, and whether they converge. To add…
Federated Learning