A communication-efficient distributed algorithm for large-scale classification within P2P networks

Amiza Amir, Balasubramaniam Srinivasan, Asad Iqbal Khan

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

    2 Citations (Scopus)


    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.
    Original languageEnglish
    Title of host publicationProceedings of the Fifth Symposium on Information and Communication Technology (SoICT 2015)
    EditorsLuc De Raedt, Yves Deville, Marc Bui, Dieu Linh Truong
    Place of PublicationNew York NY USA
    PublisherAssociation for Computing Machinery (ACM)
    Number of pages8
    ISBN (Print)9781450338431
    Publication statusPublished - 2015
    EventInternational Symposium on Information and Communication Technology 2015 - Hue, Vietnam
    Duration: 3 Jan 20154 Dec 2015
    Conference number: 6th


    ConferenceInternational Symposium on Information and Communication Technology 2015
    Abbreviated titleSoICT 2015
    Internet address

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