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APCSMA: Adaptive Personalized Client-Selection and Model-Aggregation Algorithm for Federated Learning in Edge

Xueting Ma1,2, Guorui Ma1, Yang Liu3

  • 1School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, China.

Entropy (Basel, Switzerland)
|August 29, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an Adaptive Personalized Client-Selection and Model-Aggregation Algorithm (APCSMA) to improve Federated Learning (FL) in edge computing. APCSMA enhances model accuracy by adaptively selecting clients and aggregating their contributions effectively.

Keywords:
client selectionedge computingfederated learningmodel aggregation

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Area of Science:

  • Machine Learning
  • Distributed Systems
  • Edge Computing

Background:

  • Centralized machine learning faces challenges with large datasets.
  • Federated Learning (FL) offers privacy-preserving distributed training.
  • Client heterogeneity in edge computing hinders FL performance.

Purpose of the Study:

  • To introduce an Adaptive Personalized Client-Selection and Model-Aggregation Algorithm (APCSMA).
  • To optimize FL performance in heterogeneous edge computing environments.
  • To address the impact of client heterogeneity on model accuracy.

Main Methods:

  • Developed APCSMA to evaluate client contributions using local model performance and cosine similarity.
  • Designed a ContriFunc to quantify client contributions for selection and aggregation.
  • Implemented personalized local model updates instead of direct global model overwrites.

Main Results:

  • Experiments on FashionMNIST and Cifar-10 datasets demonstrated accuracy improvements.
  • FashionMNIST saw accuracy gains of 3.9%, 1.9%, and 1.1% across different data distributions.
  • Cifar-10 achieved significant accuracy increases of 31.9%, 8.4%, and 5.4%.

Conclusions:

  • APCSMA effectively enhances FL performance in edge computing settings.
  • The algorithm successfully mitigates the negative impact of client heterogeneity.
  • Personalized updates and adaptive aggregation lead to superior model accuracy.