Privacy-Preserving Federated Primal-Dual Learning for Non-Convex Problems With Non-Smooth Regularization

Yiwei Li, Chien Wei Huang, Shuai Wang, Chong Yung Chi, Tony Q.S. Quek

研究成果: Conference contribution

2 引文 斯高帕斯(Scopus)

摘要

Recently, the federated learning (FL) has been a machine learning paradigm for the preservation of data privacy, though high communication cost and privacy protection are still the main concerns of FL. However, in many practical applications, the trained model needs certain nature or characteristics, such as sparseness in classification, otherwise learning performance loss is inevitable. In order to upgrade the learning performance, a suitable non-smooth regularizer (e.g., ℓ1-norm for the model sparseness) can be added to the loss function (often non-convex) in the considered optimization problem. This paper proposes a novel primal-dual learning algorithm to handle such non-smooth regularization aided non-convex FL problems, that yields much superior learning performance over some state-of-the-art FL algorithms under privacy guarantee by means of differential privacy. Finally, some experimental results are provided to demonstrate the efficacy of the proposed algorithm.

原文English
主出版物標題Proceedings of the 2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing, MLSP 2023
編輯Danilo Comminiello, Michele Scarpiniti
發行者IEEE Computer Society
ISBN(電子)9798350324112
DOIs
出版狀態Published - 2023
事件33rd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2023 - Rome, Italy
持續時間: 2023 9月 172023 9月 20

出版系列

名字IEEE International Workshop on Machine Learning for Signal Processing, MLSP
2023-September
ISSN(列印)2161-0363
ISSN(電子)2161-0371

Conference

Conference33rd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2023
國家/地區Italy
城市Rome
期間23-09-1723-09-20

All Science Journal Classification (ASJC) codes

  • 人機介面
  • 訊號處理

指紋

深入研究「Privacy-Preserving Federated Primal-Dual Learning for Non-Convex Problems With Non-Smooth Regularization」主題。共同形成了獨特的指紋。

引用此