2008/12/31 by Francesco Dinuzzo, Gianluigi Pillonetto, Giuseppe De Nicolao · 2 citations
Computer Science · #Architecture #Artificial intelligence #Computer science #Data Stream Mining Techniques #Data mining #Exploit #Machine Learning and Data Classification #Machine learning #Multi-task learning #Privacy-Preserving Technologies in Data #Regularization (linguistics) #Task (project management) #cs.AI #cs.LG
paper · pdf · doi:10.1109/tnn.2010.2095882
arxiv created 2010/01/11 · openalex publication_date 2011/02/01 · arxiv updated 2013/07/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.