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Related Experiment Video

Updated: Mar 12, 2026

A Method for Remotely Silencing Neural Activity in Rodents During Discrete Phases of Learning
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A Method for Remotely Silencing Neural Activity in Rodents During Discrete Phases of Learning

Published on: June 22, 2015

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Privacy Preserving Decentralized Learning With Positive-Incentive Noise.

Luqing Wang, Shaofu Yang, Yifan Wan

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |March 10, 2026
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    Summary
    This summary is machine-generated.

    This study introduces Positive-Incentive Noise Generator (PING) and PP-DPIN for private decentralized learning. These methods enhance privacy guarantees and convergence rates while defending against inference attacks.

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    A Method for Remotely Silencing Neural Activity in Rodents During Discrete Phases of Learning
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    A Method for Remotely Silencing Neural Activity in Rodents During Discrete Phases of Learning

    Published on: June 22, 2015

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    Area of Science:

    • Computer Science
    • Machine Learning
    • Cybersecurity

    Background:

    • Decentralized learning faces privacy challenges due to local data sensitivity.
    • The privacy-utility tradeoff hinders the effectiveness of privacy-preserving algorithms.
    • Colluding inference attacks pose a significant threat to decentralized systems.

    Purpose of the Study:

    • To develop a novel mechanism (PING) that mitigates the negative impact of privacy noise on convergence in decentralized learning.
    • To propose a privacy-preserving algorithm (PP-DPIN) that defends against sophisticated inference attacks.
    • To provide robust privacy quantification and analyze convergence rates for decentralized learning.

    Main Methods:

    • Introduction of the Positive-Incentive Noise Generator (PING) utilizing network topologies and encryption.
    • Development of the PP-DPIN algorithm integrating differential privacy and differential information entropy.
    • Establishment of convergence rates under stochastic convex and nonconvex settings.

    Main Results:

    • PING generates correlated noise, preserving convergence while defending against attacks.
    • PP-DPIN offers strong privacy guarantees for at least half of the nodes.
    • Demonstrated linear speedup relative to network size and superior performance in computer vision tasks.

    Conclusions:

    • PING and PP-DPIN effectively address the privacy-utility tradeoff in decentralized learning.
    • The proposed methods provide robust privacy guarantees and improved convergence.
    • PP-DPIN shows superior performance and robustness against attacks compared to existing methods.