Federated Learning Inspired Fuzzy Systems: Decentralized Rule Updating for Privacy and Scalable Decision Making

TL;DR

This paper explores integrating federated learning concepts into fuzzy systems to enable decentralized rule updating, aiming to enhance privacy, scalability, and decision-making capabilities.

Contribution

It proposes a novel approach to incorporate federated learning-inspired decentralized rule updating in fuzzy systems, addressing privacy and scalability issues.

Findings

Potential for improved privacy preservation in fuzzy systems

Enhanced scalability through decentralized rule updates

Foundation for further investigation into federated fuzzy systems

Abstract

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.