中文标题#
基於自進化高斯聚類的聯邦學習
英文标题#
Federated Learning based on Self-Evolving Gaussian Clustering
中文摘要#
在本研究中，我們提出了一種在聯邦學習背景下的動態模糊系統，該系統能夠隨著新聚類的添加而動態適應，因此不需要預先選擇聚類的數量。 與傳統方法不同，聯邦學習允許模型在客戶端設備上本地訓練，僅與中央伺服器共享模型參數而非數據。 我們的方法使用 PyTorch 實現，在聚類和分類任務上進行了測試。 結果表明，我們的方法在幾個著名的 UCI 數據集上優於現有的分類方法。 儘管由於重疊條件計算而計算量較大，但所提出的方法在去中心化數據處理中表現出顯著優勢。
英文摘要#
In this study, we present an Evolving Fuzzy System within the context of Federated Learning, which adapts dynamically with the addition of new clusters and therefore does not require the number of clusters to be selected apriori. Unlike traditional methods, Federated Learning allows models to be trained locally on clients' devices, sharing only the model parameters with a central server instead of the data. Our method, implemented using PyTorch, was tested on clustering and classification tasks. The results show that our approach outperforms established classification methods on several well-known UCI datasets. While computationally intensive due to overlap condition calculations, the proposed method demonstrates significant advantages in decentralized data processing.
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