document: Distributed cooperative mining for information consortia We consider the situation where a number of agents are distributed and each of them collects a data sequence generated according to an unknown probability distribution . Here each of the distributions is specified by common parameters and individual parameters e.g. , a normal distribution with an identical mean and a different variance . Here we introduce a notion of an information consortium , which is a framework where the agents can not show raw data to one another , but they like to enjoy significant information gain for estimating the respective distributions . Such an information consortium has recently received much interest in a broad range of areas including financial risk management , ubiquitous network mining , etc. . In this paper we are concerned with the following three issues : 1 ) how to design a collaborative strategy for agents to estimate the respective distributions in the information consortium , 2 ) characterizing when each agent has a benefit in terms of information gain for estimating its distribution or information loss for predicting future data , and 3 ) charracterizing how much benefit each agent obtains . In this paper we yield a statistical formulation of information consortia and solve all of the above three problems for a general form of probability distributions . Specifically we propose a basic strategy for cooperative estimation and derive a necessary and sufficient condition for each agent to have a significant benefit .
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