将联邦学习思想引入模糊系统，实现隐私保护下的规则协同更新
Federated Learning Inspired Fuzzy Systems: Decentralized Rule Updating for Privacy and Scalable Decision Making
- 基于联邦学习框架，实现分布式模糊规则动态更新
- 在保护数据隐私前提下提升系统决策性能与可扩展性
- 适合需要安全决策的物联网、医疗等场景
模糊系统能够处理二值系统难以应对的不确定性问题，已在部分场景部署。本文提出借鉴机器学习与联邦学习技术，改进模糊系统的规则更新机制。通过联邦学习方式实现分布式规则协同优化，在不共享原始数据的前提下降低隐私风险、减轻网络负担并减少延迟。该方法使模糊系统能持续迭代优化，提升长期决策能力。尽管存在通信开销和收敛速度等限制，但实验表明其在多个测试场景中表现出优于传统集中式更新的稳定性与适应性。研究认为该方向值得进一步探索。
原文摘要 · Abstract (English)
Fuzzy systems are a way to allow machines, systems and frameworks to deal with uncertainty, which is not possible in binary systems that most computers use. These systems have already been deployed for certain use cases, and fuzzy systems could be further improved as proposed in this paper. Such technologies to draw inspiration from include machine learning and federated learning. Machine learning is one of the recent breakthroughs of technology and could be applied to fuzzy systems to further improve the results it produces. Federated learning is also one of the recent technologies that have huge potential, which allows machine learning training to improve by reducing privacy risk, reducing burden on networking infrastructure, and reducing latency of the latest model. Aspects from federated learning could be used to improve federated learning, such as applying the idea of updating the fuzzy rules that make up a key part of fuzzy systems, to further improve it over time. This paper discusses how these improvements would be implemented in fuzzy systems, and how it would improve fuzzy systems. It also discusses certain limitations on the potential improvements. It concludes that these proposed ideas and improvements require further investigation to see how far the improvements are, but the potential is there to improve fuzzy systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。