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Information Bottleneck Classification in Extremely Distributed Systems.

Denis Ullmann1, Shideh Rezaeifar1, Olga Taran1

  • 1SIP-Stochastic Information Processing Group, Computer Science Department CUI, University of Geneva, Route de Drize 7, 1227 Carouge, Switzerland.

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Summary
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We developed a novel decentralized classification system using distributed nodes and a central server. This approach offers promising performance for big data and private classification tasks.

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Area of Science:

  • Computer Science
  • Machine Learning
  • Artificial Intelligence

Background:

  • Traditional classification systems face challenges with big data and privacy concerns.
  • Decentralized systems offer potential solutions for distributed data processing and analysis.

Purpose of the Study:

  • To introduce a new decentralized classification system leveraging a distributed architecture.
  • To address big-data communication challenges and enable private classification.

Main Methods:

  • A system with distributed nodes, each having unique datasets and computing modules.
  • Nodes utilize auto-encoders with fixed encoders, pre-trained quantizers, and class-dependent decoders.
  • A centralized server aggregates node responses for final classification based on minimum reconstruction distortion.

Main Results:

  • The system demonstrated promising performance on benchmark datasets like MNIST and FashionMNIST.
  • The auto-encoder's reconstruction distortion is minimized when data distribution matches training data.
  • A mismatch in data distribution leads to increased reconstruction distortion, aiding classification.

Conclusions:

  • The proposed decentralized system is effective for classification tasks, especially in big-data scenarios.
  • The architecture provides a theoretical link to the information bottleneck principle.
  • The system is suitable for applications requiring private data classification.