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Learning from history for personalized federated learning
Neural NetworksPersonalized Federated Learning (pFL) has received extensive attentions, due to its ability to effectively process non-IID data distributed among different clients. However, most of the existing pFL methods focus on the collaboration between global and local models to enrich the personalization process, but ignoring a lot of valuable historical ...
Yingxun Fu, Shulan Yin, Li Ma, Jie Liu
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TVZ - Verpleegkunde in praktijk en wetenschap, 2020
Schalkwijk, Hugo +3 more
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Schalkwijk, Hugo +3 more
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In this demo, we will show how Active Learning (AL) can be used to establish and transfer classification information over partially/loosely related datasets, in particular fine-grained user roles on social media, with many and unbalanced classes, large number of data points as well as different internal structures or drifts over time.
Johannes Kastner +3 more
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Johannes Kastner +3 more
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Reinforcement learning based automated history matching for improved hydrocarbon production forecast
Applied Energy, 2021Siddharth Misra
exaly
The effect of online learning in modern history education
Heliyon, 2022Elena Tokareva, Larisa Orchakova
exaly