Communication-Efficient Classification in P2P Networks

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.