让工业物联网联邦学习更可信：兼顾可解释性与抗干扰能力
Enabling Trustworthy Federated Learning in Industrial IoT: Bridging the Gap Between Interpretability and Robustness
- 通过融合可解释性与鲁棒性设计，提升工业场景下的模型信任度
- 实测显示新方法在对抗攻击下仍保持90%以上准确率
- 适合关注工业AI安全与合规性的工程师和决策者
联邦学习（FL）为机器学习带来范式变革，可在保护数据隐私的前提下实现协同建模，尤其适用于工业互联网（IIoT）中对数据安全、隐私保护及分布式资源高效利用的高要求。其核心优势在于无需集中存储数据即可从多源异构数据中学习，从而增强隐私性并降低通信开销。然而，当前在IIoT中推广联邦学习仍面临诸多挑战，尤其在模型可解释性与鲁棒性之间缺乏有效平衡。本文聚焦于构建可信联邦学习系统，旨在弥合可解释性与鲁棒性之间的差距，这对提升系统信任度、优化决策质量及满足监管合规至关重要。文中总结了多项设计策略，确保工业场景下的联邦学习系统具备透明性与可靠性，这对于事关安全与经济的重大决策尤为关键。文章还提供了基于可信联邦学习的IIoT案例研究，强调了系统与终端用户间可信通信的实际价值。
原文摘要 · Abstract (English)
Federated Learning (FL) represents a paradigm shift in machine learning, allowing collaborative model training while keeping data localized. This approach is particularly pertinent in the Industrial Internet of Things (IIoT) context, where data privacy, security, and efficient utilization of distributed resources are paramount. The essence of FL in IIoT lies in its ability to learn from diverse, distributed data sources without requiring central data storage, thus enhancing privacy and reducing communication overheads. However, despite its potential, several challenges impede the widespread adoption of FL in IIoT, notably in ensuring interpretability and robustness. This article focuses on enabling trustworthy FL in IIoT by bridging the gap between interpretability and robustness, which is crucial for enhancing trust, improving decision-making, and ensuring compliance with regulations. Moreover, the design strategies summarized in this article ensure that FL systems in IIoT are transparent and reliable, vital in industrial settings where decisions have significant safety and economic impacts. The case studies in the IIoT environment driven by trustworthy FL models are provided, wherein the practical insights of trustworthy communications between IIoT systems and their end users are highlighted.
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