National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, China

Data in the Internet are scattered on different sites indeliberately, and accumulated and updated frequently but not synchronously. It is infeasible to collect all the data together to train a global learner for prediction; even exchanging learners trained on different sites is costly. In this paper, aggregative-learning is proposed. In this paradigm, every site maintains a local learner trained from its own data. Upon receiving a request for prediction, an aggregative-learner at a local site activates and sends out many mobile agents taking the request to potential remote learners. The prediction of the aggregative-learner is made by combining the local prediction and the responses brought back by the agents. Theoretical analysis and simulation experiments show the superiority of the proposed method.

This work was partially supported by the National Natural Science Foundation of China (Grant Nos. 60635030, 60721002), and Jiangsu Science Foundation (Grant No. BK2008018).