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SEMANTIC LEARNING IN FEDERATED LEARNING SYSTEM

机译:联邦学习系统中的语义学习

摘要

A method, a computer system, and a computer program product are provided for federated learning enhanced with semantic learning. An aggregator may receive cluster information from distributed computing devices. The cluster information may relate to identified clusters in sample data of the distributed computing devices. The aggregator may integrate the cluster information to define classes. The integrating may include identifying any redundant clusters amongst the identified clusters. A number of the classes may correspond to a total number of the clusters from the distributed computing devices reduced by any redundant clusters. A deep learning model may be sent from the aggregator to the distributed computing devices. The deep learning model may include an output layer having nodes that may correspond to the defined classes. The aggregator may receive results of federated learning performed by the distributed computing devices. The federated learning may train the deep learning model.
机译:本发明提供了一种用于通过语义学习增强的联邦学习的方法、计算机系统和计算机程序产品。聚合器可以从分布式计算设备接收集群信息。集群信息可以与分布式计算设备的样本数据中识别的集群相关。聚合器可以集成集群信息来定义类。集成可包括识别所识别集群中的任何冗余集群。多个类可以对应于由任何冗余集群减少的分布式计算设备的集群总数。深度学习模型可以从聚合器发送到分布式计算设备。深度学习模型可包括输出层,其具有可对应于所定义的类的节点。聚合器可以接收由分布式计算设备执行的联合学习的结果。联邦学习可以训练深度学习模型。

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