A Communication-Efficient Distributed Algorithm for Large-scale Classification within P2P Networks

This paper proposes a supervised and fully-distributed intelligent classification algorithm that is accurate and scalable for large networks. In addition, the resulting algorithm has the following interesting features: fully-distributed, asynchronous, light-weight, online learning, and fast responses. These characteristics make it scalable for large networks. A major distinction of our method compared to the other approaches is that it forms a single global classifier, instead of building many local classifiers (one at every site). Fine-granularity components of the classifier are distributed across the network by using Distributed Hash Table (DHT) --- which provides efficient linking to these components and ensures the system remains fully-distributed. Our simulation results also show that the proposed method is more communication-efficient than several other distributed algorithms. The results also show that the distributed algorithm is able to produce accurate results that are comparable to the available state-of-the-art machine learning techniques.