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.

翻译：联邦学习是一种分布式机器学习技术，实现了数据隐私保护与数据共享计算之间的平衡。为保护数据隐私，联邦学习通过在参与设备上本地执行分布式训练，并将局部模型聚合成全局模型来学习共享模型。联邦学习中存在一个问题，即不同用户终端间数据的非独立同分布所产生的负面影响。为缓解该问题，本文提出一种基于自适应OPTICS聚类的强化联邦聚合方法。具体而言，该方法将聚类环境感知为马尔可夫决策过程，对参数搜索方向的调整过程进行建模，从而寻找最佳聚类参数以实现最优联邦聚合方法。本文的核心贡献在于提出一种适用于联邦学习的自适应OPTICS聚类算法。该算法融合了OPTICS聚类与自适应学习技术，能有效处理不同用户终端间非独立同分布数据的问题。通过将聚类环境感知为马尔可夫决策过程，目标是在无需人工辅助的情况下找到OPTICS聚类的最优参数，从而获得最佳联邦聚合方法并实现更优性能。实验数据验证了该方法的可靠性和实用性，证明了其有效性和优越性。