Efficient Peer-to-Peer Belief Propagation

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Abstract

In this paper, we will present an efficient approach for distributed inference. We use belief propagation’s message-passing algorithm on top of a DHT storing a Bayesian network. Nodes in the DHT run a variant of the spring relaxation algorithm to redistribute the Bayesian network among them. Thereafter correlated data is stored close to each other reducing the message cost for inference. We simulated our approach in Matlab and show the message reduction and the achieved load balance for random, tree-shaped, and scale-free Bayesian networks of different sizes.

As possible application, we envision a distributed software knowledge base maintaining encountered software bugs under users’ system configurations together with possible solutions for other users having similar problems. Users would not only be able to repair their system but also to foresee possible problems if they would install software updates or new applications.