Reinforcement Federated Learning Method Based on Adaptive OPTICS Clustering

TL;DR

This paper introduces an adaptive OPTICS clustering-based federated learning method that dynamically adjusts clustering parameters to improve model aggregation in non-i.i.d. data environments, enhancing privacy and performance.

Contribution

It proposes a novel adaptive OPTICS clustering algorithm integrated with federated learning, modeling parameter adjustment as a Markov decision process for optimal aggregation.

Findings

Effective handling of non-i.i.d. data across devices.

Improved federated model accuracy and robustness.

Validated through experimental data showing superiority.

Abstract

Federated learning is a distributed machine learning technology, which realizes the balance between data privacy protection and data sharing computing. To protect data privacy, feder-ated learning learns shared models by locally executing distributed training on participating devices and aggregating local models into global models. There is a problem in federated learning, that is, the negative impact caused by the non-independent and identical distribu-tion of data across different user terminals. In order to alleviate this problem, this paper pro-poses a strengthened federation aggregation method based on adaptive OPTICS clustering. Specifically, this method perceives the clustering environment as a Markov decision process, and models the adjustment process of parameter search direction, so as to find the best clus-tering parameters to achieve the best federated aggregation method. The core contribution of this paper is to propose an adaptive OPTICS clustering algorithm for federated learning. The algorithm combines OPTICS clustering and adaptive learning technology, and can effective-ly deal with the problem of non-independent and identically distributed data across different user terminals. By perceiving the clustering environment as a Markov decision process, the goal is to find the best parameters of the OPTICS cluster without artificial assistance, so as to obtain the best federated aggregation method and achieve better performance. The reliability and practicability of this method have been verified on the experimental data, and its effec-tiveness and superiority have been proved.