Momentum Gradient Descent Federated Learning with Local Differential Privacy
Nowadays, the development of information technology is growing rapidly. In the big data era, the privacy of personal information has been more pronounced. The major challenge is to find a way to guarantee that sensitive personal information is not disclosed while data is published and analyzed. Centralized differential privacy is established on the assumption of a trusted third-party data curator. However, this assumption is not always true in reality. As a new privacy preservation model, local differential privacy has relatively strong privacy guarantees. Although federated learning has relatively been a privacy-preserving approach for distributed learning, it still introduces various privacy concerns. To avoid privacy threats and reduce communication costs, in this article, we propose integrating federated learning and local differential privacy with momentum gradient descent to improve the performance of machine learning models.
Code (0)
등록된 구현이 없습니다.
Tasks
Federated LearningPrivacy PreservingSimilar Papers 제목 키워드 기반
Accelerating Federated Learning via Momentum Gradient Descent
Federated learning (FL) provides a communication-efficient approach to solve machine learning problems concerning distributed data, without sending raw data to a central server. However, existing works on FL only utilize…
BIG-bench Machine LearningFederated LearningFedCM: Federated Learning with Client-level Momentum
Federated Learning is a distributed machine learning approach which enables model training without data sharing. In this paper, we propose a new federated learning algorithm, Federated Averaging with Client-level Momentu…
Federated LearningAttacks to Federated Learning: Responsive Web User Interface to Recover Training Data from User Gradients
Local differential privacy (LDP) is an emerging privacy standard to protect individual user data. One scenario where LDP can be applied is federated learning, where each user sends in his/her user gradients to an aggrega…
Federated LearningRethinking the initialization of Momentum in Federated Learning with Heterogeneous Data
Data Heterogeneity is a major challenge of Federated Learning performance. Recently, momentum based optimization techniques have beed proved to be effective in mitigating the heterogeneity issue. Along with the model upd…
Federated LearningFederated Stochastic Gradient Descent Begets Self-Induced Momentum
Federated learning (FL) is an emerging machine learning method that can be applied in mobile edge systems, in which a server and a host of clients collaboratively train a statistical model utilizing the data and computat…
Federated Learning