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.
The work presented in this paper was supported (in part) by the National Competence Center in Research on Mobile Information and Communication Systems (NCCR-MICS), a center supported by the Swiss National Science Foundation under grant number 5005-67322 and was (partly) carried out in the framework of the EPFL Center for Global Computing and supported by the Swiss National Funding Agency OFES as part of the European project NEPOMUK No FP6-027705.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/11914853_71.
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Schmidt, R., Aberer, K. (2006). Efficient Peer-to-Peer Belief Propagation. In: Meersman, R., Tari, Z. (eds) On the Move to Meaningful Internet Systems 2006: CoopIS, DOA, GADA, and ODBASE. OTM 2006. Lecture Notes in Computer Science, vol 4275. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11914853_31
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DOI: https://doi.org/10.1007/11914853_31
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