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Client–Server Multitask Learning From Distributed Datasets

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83886

Dinuzzo,  F
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Dinuzzo, F., Pillonetto, G., & De Nicolao, G. (2011). Client–Server Multitask Learning From Distributed Datasets. IEEE Transactions on Neural Networks, 22(2), 290-303. doi:10.1109/TNN.2010.2095882.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-BC84-B
Zusammenfassung
A client-server architecture to simultaneously solve multiple learning tasks from distributed datasets is described. In such architecture, each client corresponds to an individual learning task and the associated dataset of examples. The goal of the architecture is to perform information fusion from multiple datasets while preserving privacy of individual data. The role of the server is to collect data in real time from the clients and codify the information in a common database. Such information can be used by all the clients to solve their individual learning task, so that each client can exploit the information content of all the datasets without actually having access to private data of others. The proposed algorithmic framework, based on regularization and kernel methods, uses a suitable class of “mixed effect” kernels. The methodology is illustrated through a simulated recommendation system, as well as an experiment involving pharmacological data coming from a multicentric clinical trial.