隨著物聯網設備的增加，越來越多設備參與進機器學習中，但其中存在的隱私洩漏的風險。為了保證隱私權，聯邦式學習被提出。然而在實際行使上會遇到以下四個問題：通信成本、數據異質性、設備異質性、隱私安全。本論文中提出針對聯邦式學習的集群算法，試圖解決數據異質性的問題，並更進一步提出能減少分群時負擔的算法。分群方式採用一種自動的分群方法，而非硬性規定集群的數量。在結果中顯示該算法在特定的客戶端分布中具有強大的性能，能顯著提升訓練模型最終的精準度。

With the increase of IoT devices, more and more devices are involved in machine learning, but there is a risk of privacy leakage. In order to ensure privacy, federated learning is proposed. However, four problems are encountered in the practical implementation: communication cost, data heterogeneity, device heterogeneity, and privacy security. In this paper, we propose a clustering algorithm for federated learning to solve the problem of data heterogeneity and further propose an algorithm to reduce the burden of clustering. The clustering approach uses an automatic clustering method rather than a rigid specification of the number of clusters. In the results, it is shown that the algorithm has a strong performance in a specific client distribution and can significantly improve the final accuracy of the trained model.

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