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IFIP Advances in Information and Communication Technology
In this paper we consider large scale distributed committee machines where no local data exchange is possible between neural network modules. Regularization neural networks are used for both the modules as well as the combiner committee in an embedded architecture. After the committee training no module will know anything else except its own local data. This privacy preserving obligation is a challenging problem for trainable combiners but crucial in real world applications. Only classifiers indoi:10.1007/978-3-642-33409-2_11 fatcat:qkfccow3znfozcxkxumxjv6pna