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Related Experiment Video

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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
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Personalized federated learning for heterogeneous data: A distributed edge clustering approach.

Muhammad Firdaus1, Siwan Noh2, Zhuohao Qian2

  • 1Department of Artificial Intelligence Convergence, Pukyong National University, Busan 48513, Republic of Korea.

Mathematical Biosciences and Engineering : MBE
|June 16, 2023
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Summary

Personalized federated learning (PFL) improves model convergence with heterogeneous data. This study introduces a blockchain-enabled distributed edge cluster for PFL (BPFL) to enhance privacy, security, and real-time performance.

Keywords:
blockchainclient clusteringedge computingnon-IID datapersonalized FL

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Area of Science:

  • Distributed Machine Learning
  • Artificial Intelligence
  • Cybersecurity

Background:

  • Federated learning (FL) enables collaborative model training across devices while preserving data privacy.
  • Data heterogeneity in FL leads to poor model convergence, necessitating personalized federated learning (PFL).
  • Existing clustering-based PFL methods often rely on centralized server coordination.

Purpose of the Study:

  • To address the limitations of centralized PFL approaches.
  • To introduce a novel blockchain-enabled distributed edge cluster for PFL (BPFL).
  • To enhance privacy, security, and real-time performance in PFL systems.

Main Methods:

  • Integration of blockchain technology for secure, immutable transaction recording, improving client selection and clustering.
  • Leveraging edge computing for localized storage and computation, bringing processing closer to clients.
  • Development of a decentralized architecture for PFL coordination.

Main Results:

  • The proposed BPFL framework combines blockchain and edge computing benefits for improved PFL.
  • Enhanced client privacy and security through blockchain's distributed ledger.
  • Improved real-time services and low-latency communication due to edge computing's proximity to clients.

Conclusions:

  • BPFL offers a promising decentralized approach to overcome challenges in personalized federated learning.
  • The integration of blockchain and edge computing significantly enhances PFL's efficiency and security.
  • Further research is needed to develop datasets for evaluating attacks and defenses in BPFL protocols.