Federated Learning using the Federated Averaging algorithm has shown great advantages for large-scale applications that rely on collaborative learning, especially when the training data is either unbalanced or inaccessible due to privacy constraints. We hypothesize that Federated Averaging underestimates the ...
Federated learning is one popular paradigm to train a joint model in a distributed, privacy-preserving environment. But partial annotations pose an obstacle meaning that categories of labels are heterogeneous over clients. We propose to learn a joint backbone in a federated manner, while each site ...
There has been a surge of interest in continual learning and federated learning, both of which are important in deep neural networks in real-world scenarios. Yet little research has been done regarding the scenario where each client learns on a sequence of tasks from a private local data stream...
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In federated learning, client models are often trained on local training sets that vary in size and distribution. Such statistical heterogeneity in trainin
Revisiting Weighted Aggregation in Federated Learning with Neural Networks 搁浅 4 人赞同了该文章 神经网络联合学习中的加权聚合研究 在联邦学习(FL)中,对局部模型进行加权聚合,生成全局模型,聚合权值归一化(权值之和为1),并与局部数据大小成比例。在本文中,我们重新研究了加权聚合过程,并对FL的训练动力学有了新...
There has been a surge of interest in continual learning and federated learning, both of which are important in deep neural networks in real-world scenarios. Yet little research has been done regarding the scenario where each client learns on a sequence of tasks from a private local data stream...
There has been a surge of interest in continual learning and federated learning, both of which are important in deep neural networks in real-world scenarios. Yet little research has been done regarding the scenario where each client learns on a sequence of tasks from a private local data stream...