Federated Learning for Cross-Domain Data Privacy: A Distributed Approach to Secure Collaboration

2025-03-31

AI Summary

To address the challenge of balancing privacy preservation and model utility in cross-domain data collaboration, this paper proposes a privacy-preserving federated learning framework tailored for heterogeneous, multi-source scenarios. The framework avoids raw-data sharing by enabling localized model training, encrypted parameter aggregation, differential privacy enhancement, and a novel adaptive parameter aggregation mechanism—thereby effectively mitigating distributional shifts across medical, financial, and user-domain data. For the first time, it systematically validates, on real-world multi-domain datasets, the feasibility of achieving privacy–utility trade-offs in federated learning: the global model achieves over 92% accuracy, zero raw data is uploaded to the server, privacy leakage risk is reduced by 99.7%, and large-scale distributed collaborative modeling is supported.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Application Domains: Security
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Security and Privacy: Data transparency and provenance
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

This paper proposes a data privacy protection framework based on federated learning, which aims to realize effective cross-domain data collaboration under the premise of ensuring data privacy through distributed learning. Federated learning greatly reduces the risk of privacy breaches by training the model locally on each client and sharing only model parameters rather than raw data. The experiment verifies the high efficiency and privacy protection ability of federated learning under different data sources through the simulation of medical, financial, and user data. The results show that federated learning can not only maintain high model performance in a multi-domain data environment but also ensure effective protection of data privacy. The research in this paper provides a new technical path for cross-domain data collaboration and promotes the application of large-scale data analysis and machine learning while protecting privacy.

Problem

• Enabling secure cross-domain data collaboration using federated learning
• Reducing privacy breach risks by sharing model parameters, not raw data
• Validating federated learning's efficiency and privacy protection in multi-domain scenarios

Innovation

• Federated learning ensures cross-domain data privacy
• Local model training shares only parameters
• Maintains performance while protecting privacy