论文标题
联邦学习中的本地聚类异常
Abnormal Local Clustering in Federated Learning
论文作者
论文摘要
Federated Learning是隐私的模型，而无需通过转移模型揭示私人数据，而不是来自本地客户端设备的个人和私人数据。而在全球模型中，识别每个本地数据是正常的至关重要的。本文提出了一种方法，通过通过在本地模型中输入虚拟数据提取的矢量的欧几里得相似性聚类来分离正常的当地人和异常当地人。在联邦分类模型中，该方法将当地人分为正常和异常。
Federated learning is a model for privacy without revealing private data by transfer models instead of personal and private data from local client devices. While, in the global model, it's crucial to recognize each local data is normal. This paper suggests one method to separate normal locals and abnormal locals by Euclidean similarity clustering of vectors extracted by inputting dummy data in local models. In a federated classification model, this method divided locals into normal and abnormal.
