Work

Differential Privacy in Federated Learning: An Evolutionary Game Analysis

Year: 2025

Type: article

Abstract: This paper examines federated learning, a decentralized machine learning paradigm, focusing on privacy challenges. We introduce differential privacy mechanisms to protect privacy and quantify their impact on global model performance. Using evolutionary game theory, we establish a framework to analyze strategy dynamics and define utilities for different strategies based on Gaussian noise powers and training iterations. A differential privacy federated learning model (DPFLM) is analyzed within this framework. A key contribution is the thorough existence and stability analysis, identifying evolutionarily stable strategies (ESSs) and confirming their stability through simulations. This research provides theoretical insights for enhancing privacy protection in federated learning systems.

Source: Applied Sciences

DOI: 10.3390/app15062914

Authors: Zhengwei Ni, Qi Zhou

Institutions: China University of Mining and Technology, Zhejiang Gongshang University

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

OpenAlex ID: W4408167363

FWCI: 4.156

Cites: 42

Cited by: 10

Related to: 10