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<p>Federated learning (FL) enables collaborative model training without raw data sharing; however, its decentralized architecture remains vulnerable to inference attacks, malicious updates, and opaque governance. To address these challenges, we introduce an end-to-end framework integrating FL with permissioned blockchain technology, systematically guided by TRIZ innovation principles, ensuring verifiable, privacy-preserving, and ethically accountable machine learning collaborations. Our framework integrates multi-layered security measures, including encrypted local model updates, blockchain-based smart-contract consensus mechanisms for secure global aggregation, and an immutable complaint-redress system that transparently records grievances, initiates forensic audits, and documents remedial actions. Employing iterative ARIZ cycles, we effectively resolve the contradiction between strict data locality and collective model intelligence. The proposed pipeline comprises eight structured phases: cryptographic initialization, heterogeneous data preparation, dual-model instantiation, client-side optimization, weighted model aggregation, ledger anchoring, proactive dispute resolution, and comprehensive performance evaluation. Experimental evaluations on textual datasets with deliberately designed non-IID distributions demonstrate stable convergence, reduced communication overhead, and robust predictive accuracy across diverse client scenarios. Validation using recurrent neural networks and linear models for e-commerce sentiment analysis, clinical note triage, and vehicular telemetry illustrates the framework’s domain-agnostic versatility. Privacy analyses using gradient-inversion attacks and efficiency benchmarks under varied bandwidth and participation levels confirm robustness. Comparative analysis reveals our approach offers enhanced adaptability, richer analytical capabilities, and explicit ethical integration compared to existing blockchain-enhanced FL solutions. Ultimately, this research proposes a pathway towards transparent, human-centered artificial intelligence systems, harmonizing regulatory compliance, organizational objectives, and societal trust without compromising technical performance. Future studies will explore alternative blockchain consensus mechanisms, formal fairness assessments, and pilot deployments in healthcare and financial sectors.</p>

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A Novel Framework for Integrating Blockchain-Driven Federated Learning with Neural Networks in E-Commerce

  • 期刊论文

  • 发表日期：

    2025年5月22日

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  • Osama Alshareet,
  • Anjali Awasthi

摘要

Federated learning (FL) enables collaborative model training without raw data sharing; however, its decentralized architecture remains vulnerable to inference attacks, malicious updates, and opaque governance. To address these challenges, we introduce an end-to-end framework integrating FL with permissioned blockchain technology, systematically guided by TRIZ innovation principles, ensuring verifiable, privacy-preserving, and ethically accountable machine learning collaborations. Our framework integrates multi-layered security measures, including encrypted local model updates, blockchain-based smart-contract consensus mechanisms for secure global aggregation, and an immutable complaint-redress system that transparently records grievances, initiates forensic audits, and documents remedial actions. Employing iterative ARIZ cycles, we effectively resolve the contradiction between strict data locality and collective model intelligence. The proposed pipeline comprises eight structured phases: cryptographic initialization, heterogeneous data preparation, dual-model instantiation, client-side optimization, weighted model aggregation, ledger anchoring, proactive dispute resolution, and comprehensive performance evaluation. Experimental evaluations on textual datasets with deliberately designed non-IID distributions demonstrate stable convergence, reduced communication overhead, and robust predictive accuracy across diverse client scenarios. Validation using recurrent neural networks and linear models for e-commerce sentiment analysis, clinical note triage, and vehicular telemetry illustrates the framework’s domain-agnostic versatility. Privacy analyses using gradient-inversion attacks and efficiency benchmarks under varied bandwidth and participation levels confirm robustness. Comparative analysis reveals our approach offers enhanced adaptability, richer analytical capabilities, and explicit ethical integration compared to existing blockchain-enhanced FL solutions. Ultimately, this research proposes a pathway towards transparent, human-centered artificial intelligence systems, harmonizing regulatory compliance, organizational objectives, and societal trust without compromising technical performance. Future studies will explore alternative blockchain consensus mechanisms, formal fairness assessments, and pilot deployments in healthcare and financial sectors.