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<p>Federated Learning (FL) enables participants to collaboratively train a model without sharing raw data. Traditional FL models depend on a central server and full model sharing, leading to a significant issue in healthcare related to privacy concerns and communication overhead. However, to address such issues, we introduce Decentralized Federated Learning with Partial Head Sharing <b>DFL-PHS</b>, a framework that eliminates the central server, allowing peer-to-peer model updates sharing. Instead of sharing entire model parameters, participants only share a subset of weights from the final dense layers, minimizing information breach while maintaining the model’s performance. We apply our proposed framework to COVID-19 chest X-ray binary classification, where we conduct a comparative study across four scenarios: local training, centralized FL (CFL), single-site training (SST) and DFL-PHS. Evaluations are conducted on three dataset sizes (small, medium, large) and four partial sharing ratios (25%, 50%, 75%, 100%). We also assess privacy through a Membership-Inference Attack (MIA), with only 25% head sharing, the attack operates at or below chance (ROC-AUC 0.37 with advantage -0.30 on the small scale and ROC–AUC <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\approx 0.50\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mo>≈</mo> <mn>0.50</mn> </mrow> </math></EquationSource> </InlineEquation> with advantage <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\approx 0.00\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mo>≈</mo> <mn>0.00</mn> </mrow> </math></EquationSource> </InlineEquation> on the large scale), indicating no reliable membership signal. Empirical findings indicate that DFL-PHS, in many configurations, competes with or outperforms CFL, attaining F1-scores of 0.965 and recall rates surpassing 0.98, even under minimal weight-sharing conditions. These results validate DFL-PHS as a privacy-preserving and scalable system for distributed FL. While evaluated on a single radiography dataset, our framework is dataset-agnostic; we plan broader validation on non-medical and other benchmarks as future work.</p>

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Privacy Preserving using Decentralized Federated Learning with Partial Dense-layer Weight Sharing

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月10日

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  • Reem Nabha,
  • Anis Laouiti,
  • Abed Ellatif Samhat

摘要

Federated Learning (FL) enables participants to collaboratively train a model without sharing raw data. Traditional FL models depend on a central server and full model sharing, leading to a significant issue in healthcare related to privacy concerns and communication overhead. However, to address such issues, we introduce Decentralized Federated Learning with Partial Head Sharing DFL-PHS, a framework that eliminates the central server, allowing peer-to-peer model updates sharing. Instead of sharing entire model parameters, participants only share a subset of weights from the final dense layers, minimizing information breach while maintaining the model’s performance. We apply our proposed framework to COVID-19 chest X-ray binary classification, where we conduct a comparative study across four scenarios: local training, centralized FL (CFL), single-site training (SST) and DFL-PHS. Evaluations are conducted on three dataset sizes (small, medium, large) and four partial sharing ratios (25%, 50%, 75%, 100%). We also assess privacy through a Membership-Inference Attack (MIA), with only 25% head sharing, the attack operates at or below chance (ROC-AUC 0.37 with advantage -0.30 on the small scale and ROC–AUC \(\approx 0.50\) ≈ 0.50 with advantage \(\approx 0.00\) ≈ 0.00 on the large scale), indicating no reliable membership signal. Empirical findings indicate that DFL-PHS, in many configurations, competes with or outperforms CFL, attaining F1-scores of 0.965 and recall rates surpassing 0.98, even under minimal weight-sharing conditions. These results validate DFL-PHS as a privacy-preserving and scalable system for distributed FL. While evaluated on a single radiography dataset, our framework is dataset-agnostic; we plan broader validation on non-medical and other benchmarks as future work.