融合AI技术提升联邦学习的个性化与隐私保护能力
Advancing Personalized Federated Learning: Integrative Approaches with AI for Enhanced Privacy and Customization
- 结合自适应优化、迁移学习与差分隐私增强个性化联邦学习
- 模型准确率与个性化程度显著提升，同时满足严格隐私要求
- 适合需合规数据保护的医疗、金融等敏感领域应用
在数据驱动决策的时代，保护隐私并提供个性化体验至关重要。个性化联邦学习（PFL）通过去中心化学习过程，保障数据隐私并减少对集中式数据存储的依赖。然而，先进人工智能技术在PFL中的整合仍不充分。本文提出一种新方法，融合自适应优化、迁移学习和差分隐私等前沿AI技术，提升个体客户端模型性能，确保跨异构网络的强隐私保护与高效资源利用。实证结果表明，相比传统联邦学习模型，该方法在模型准确率、个性化水平及隐私合规性方面均有显著提升。本工作为真正个性化且注重隐私的AI系统开辟了新路径，对需严守数据保护法规的行业具有重要意义。
原文摘要 · Abstract (English)
In the age of data-driven decision making, preserving privacy while providing personalized experiences has become paramount. Personalized Federated Learning (PFL) offers a promising framework by decentralizing the learning process, thus ensuring data privacy and reducing reliance on centralized data repositories. However, the integration of advanced Artificial Intelligence (AI) techniques within PFL remains underexplored. This paper proposes a novel approach that enhances PFL with cutting-edge AI methodologies including adaptive optimization, transfer learning, and differential privacy. We present a model that not only boosts the performance of individual client models but also ensures robust privacy-preserving mechanisms and efficient resource utilization across heterogeneous networks. Empirical results demonstrate significant improvements in model accuracy and personalization, along with stringent privacy adherence, as compared to conventional federated learning models. This work paves the way for a new era of truly personalized and privacy-conscious AI systems, offering significant implications for industries requiring compliance with stringent data protection regulations.
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