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- W161901664 abstract "We address the problem of learning a classifier from distributed data over a number of arbitrarily connected machines without exchange of the datapoints. Our purpose is to train a neural network at each machine as if the entire dataset was locally available. This is accomplished by taking advantage of the so called consensus algorithm for scalar values distributed over a network. We describe an abstract framework for consensus learning and we derive a distributed version for the multilayer feed forward neural networks with back-propagation and early stopping. Tests are performed and results show that with a careful selection of parameters our method performs like the non-distributed. The trade-off is that the total computational effort over all machines is larger." @default.
- W161901664 created "2016-06-24" @default.
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- W161901664 date "2011-01-01" @default.
- W161901664 modified "2023-09-27" @default.
- W161901664 title "Training Distributed Neural Networks by Consensus" @default.
- W161901664 hasPublicationYear "2011" @default.
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