Communication-Efficient Classification in P2P Networks

  • Hock Hee Ang
  • Vivekanand Gopalkrishnan
  • Wee Keong Ng
  • Steven Hoi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5781)


Distributed classification aims to learn with accuracy comparable to that of centralized approaches but at far lesser communication and computation costs. By nature, P2P networks provide an excellent environment for performing a distributed classification task due to the high availability of shared resources, such as bandwidth, storage space, and rich computational power. However, learning in P2P networks is faced with many challenging issues; viz., scalability, peer dynamism, asynchronism and fault-tolerance. In this paper, we address these challenges by presenting CEMPaR—a communication-efficient framework based on cascading SVMs that exploits the characteristics of DHT-based lookup protocols. CEMPaR is designed to be robust to parameters such as the number of peers in the network, imbalanced data sizes and class distribution while incurring extremely low communication cost yet maintaining accuracy comparable to the best-in-the-class approaches. Feasibility and effectiveness of our approach are demonstrated with extensive experimental studies on real and synthetic datasets.


Communication Cost Physical Address Prediction Phase Class Count Prediction Cost 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Hock Hee Ang
    • 1
  • Vivekanand Gopalkrishnan
    • 1
  • Wee Keong Ng
    • 1
  • Steven Hoi
    • 1
  1. 1.Nanyang Technological UniversitySingapore

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