A consensus-based approach to the distributed learning

The paper deals with the distributed learning. Distributed learning from data is considered to be an important challenge faced by researchers and practice in the domain of the distributed data mining and distributed knowledge discovery from databases. An effective approach to learning from a geographically distributed data is to select, from the local databases, relevant local patterns, called also prototypes. Such a selection can be based on results of the data reduction process. The paper proposes to carry-out prototype selection at local sites in parallel, independently at each site, employing specialized software agents. To assure obtaining homogenous prototypes at a global level the consensus-based method is proposed and applied. The paper includes a detailed description of the proposed approach and a discussion of the computational experiment results.