Multi-dimensional data density estimation in P2P networks
Distributed and Parallel Databases
RETRACTED: Impacts of sensor node distributions on coverage in sensor networks
Journal of Parallel and Distributed Computing
Personalized query evaluation in ring-based P2P networks
Information Sciences: an International Journal
The VLDB Journal — The International Journal on Very Large Data Bases
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Learning the underlying model from distributed data is often useful for many distributed systems. In this paper, we study the problem of learning a non-parametric model from distributed observations. We propose a gossip-based distributed kernel density estimation algorithm and analyze the convergence and consistency of the estimation process. Furthermore, we extend our algorithm to distributed systems under communication and storage constraints by introducing a fast and efficient data reduction algorithm. Experiments show that our algorithm can estimate underlying density distribution accurately and robustly with only small communication and storage overhead.