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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Resilient and Communication Efficient Learning for Heterogeneous Federated Systems.

Zhuangdi Zhu1, Junyuan Hong1, Steve Drew2

  • 1Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA.

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|April 14, 2023
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Federated Learning (FL) in edge computing faces challenges from network diversity and unreliable connections. This new FL scheme uses self-distilled neural networks to improve efficiency and resilience for edge devices.

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Edge Computing

Background:

  • Federated Learning (FL) enables machine learning on decentralized data from edge devices.
  • Heterogeneous network topologies and wireless transmission uncertainty hinder FL's efficiency and increase convergence time and communication costs in edge computing.

Purpose of the Study:

  • To propose a novel Federated Learning (FL) scheme that addresses system heterogeneity and wireless connection uncertainty in edge computing.
  • To enhance the convergence speed and communication efficiency of FL in challenging edge environments.

Main Methods:

  • Edge devices learn self-distilled neural networks, which are prunable to various sizes.
  • The proposed method allows partial transmission of model parameters even with faulty network connections, preserving knowledge.

Main Results:

  • The approach effectively handles system heterogeneity by supporting diverse model architectures on edge devices.
  • It demonstrates significant resilience and improved communication efficiency under network instability compared to existing methods.

Conclusions:

  • The developed FL scheme offers a robust solution for deploying machine learning in heterogeneous and unreliable edge computing environments.
  • Self-distilled, prunable neural networks are key to overcoming the limitations of traditional FL in edge applications.