Research on Privacy Preservation and Collaboration of Medical Big Data Based on Federated Learning and Edge Computing

DOI: https://doi.org/10.62517/jbdc.202601106

Author(s)

Jialin Liu

Affiliation(s)

Computer Science and Technology College, Zhejiang Normal University, Jinhua, Zhejiang, China

Abstract

The multi-source and explosive growth of medical big data presents a core challenge to achieving cross-institutional collaborative modeling while ensuring privacy and security in the context of medical intelligence. Federated Learning (FL) enables collaborative training without sharing raw data, Edge Computing (EC) improves model responsiveness and energy efficiency through near-source computation, and Differential Privacy (DP) provides quantifiable privacy protection for model updates. The integration of FL, EC, and DP offers a new system framework and research direction for the secure collaboration of medical big data. This paper systematically reviews the recent research progress on the integration of FL, EC, and DP in medical scenarios, outlines typical architectures, privacy mechanisms, and optimization strategies, and compares the trade-offs among model performance, privacy assurance, and resource overhead in different schemes. This study proposes a three-dimensional evaluation framework: "Performance-Privacy-Resource," and discusses key issues such as heterogeneous data distribution, end-edge-cloud collaboration, and privacy-performance co-optimization. The research aims to provide a systematic reference and future research directions for privacy preservation and distributed intelligent collaboration in medical big data scenarios.

Keywords

Federated Learning; Edge Computing; Medical Big Data; Differential Privacy; Privacy Preservation

References

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