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Related Concept Videos

Associative Learning01:27

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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Related Experiment Videos

Threshold-adaptive pruning with multi-key homomorphic encryption for communication-efficient secure federated

Jie Guo1, Renjing Liu1, Jinsheng Xing2

  • 1Xi'an Jiaotong University, School of Management, Xi'an, Shaanxi, China.

Plos One
|May 18, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an optimized federated learning scheme using adaptive channel pruning and multi-key homomorphic encryption to enhance communication efficiency and resist collusion attacks in edge computing. The approach balances model accuracy and compression while ensuring secure, private data aggregation.

Related Experiment Videos

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Cryptography

Background:

  • Federated learning (FL) faces communication inefficiencies due to frequent parameter exchange between edge devices and servers.
  • Existing encryption methods are vulnerable to multi-node collusion attacks, compromising data security.

Purpose of the Study:

  • To propose an optimized federated learning scheme that enhances communication efficiency and security.
  • To address the limitations of conventional encryption against collusion attacks in FL.

Main Methods:

  • Implemented adaptive channel pruning with a dynamic threshold mechanism for automatic rate calibration based on precision feedback.
  • Designed a distributed public-key encryption protocol using the Brakerski-Gentry-Vaikuntanathan (BGV) multi-key fully homomorphic encryption architecture.
  • Developed a privacy-preserving aggregation method for model parameters without decryption, resilient to collusion attacks.

Main Results:

  • Adaptive pruning significantly reduced communication bandwidth consumption and computational burden.
  • The multi-key BGV encryption scheme securely aggregated multi-source model parameters, resisting collusion from up to C-1 nodes.
  • Experiments on MNIST and CIFAR-10 datasets validated the scheme's effectiveness in reducing communication overhead and enhancing privacy.

Conclusions:

  • The proposed scheme offers a novel technical pathway for privacy-preserving federated learning in resource-constrained environments.
  • Adaptive channel pruning and multi-key BGV encryption effectively mitigate communication bottlenecks and security vulnerabilities in FL.