Scholarlink

<p>As digital marketing continues to dominate global outreach strategies, worldwide spending on digital advertising is projected to surpass $785 billion by 2026. This surge is driven by real-time data analytics, behavioral targeting, and AI-powered personalization, all of which rely heavily on the large-scale collection and processing of consumer data. However, growing regulatory pressure from data privacy laws such as GDPR and CCPA, along with increasing public scrutiny, has elevated the demand for privacy-preserving and decentralized analytics frameworks. This study introduces an adaptive federated learning FL framework tailored for ordinal classification in digital marketing environments. The proposed system integrates two ordinal classifiers CORAL and CLM with a novel adaptive aggregation strategy that assigns dynamic weights to clients based on their contribution relevance, measured via feature importance. The experimental setup simulates a realistic collaborative marketing scenario involving five federated clients, each handling either real-world as Google Merchandise Store, UK Online Retail or synthetic as influencer and email campaign datasets. Through extensive experimentation, the framework demonstrates strong generalization across synthetic and real datasets, achieving classification accuracies up to 93.9%. Scalability tests across 5, 10, 15, and 20 clients validate the robustness of the aggregation method, with performance degradation remaining within 5%. The framework is benchmarked against baseline federated strategies such as FedAVG, FedSGD, and FedProx, and is evaluated under practical constraints using deployment-aligned analysis with frameworks like NVIDIA Clara, OpenFL, and Flower. This manuscript presents multiple comprehensive experimental analyses including convergence trends, client contribution evolution, resource utilization, and fairness-aware aggregation, making it a comprehensive study on privacy-preserving, ordinal, and adaptive federated analytics for modern digital marketing systems.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Privacy-preserving federated analytics for digital marketing using adaptive weighted aggregation

  • 期刊论文

  • 发表日期：

    2026年6月17日

查看全文
  • Rahul Haripriya,
  • Nilay Khare,
  • Manish Pandey,
  • Gurleen Kaur Bhatia,
  • Ruchi Nandurkar,
  • Meghavi Hada,
  • Sreemoyee Biswas,
  • Jaytrilok Choudhary,
  • Dhirendra Pratap Singh,
  • Sumit Gupta

摘要

As digital marketing continues to dominate global outreach strategies, worldwide spending on digital advertising is projected to surpass $785 billion by 2026. This surge is driven by real-time data analytics, behavioral targeting, and AI-powered personalization, all of which rely heavily on the large-scale collection and processing of consumer data. However, growing regulatory pressure from data privacy laws such as GDPR and CCPA, along with increasing public scrutiny, has elevated the demand for privacy-preserving and decentralized analytics frameworks. This study introduces an adaptive federated learning FL framework tailored for ordinal classification in digital marketing environments. The proposed system integrates two ordinal classifiers CORAL and CLM with a novel adaptive aggregation strategy that assigns dynamic weights to clients based on their contribution relevance, measured via feature importance. The experimental setup simulates a realistic collaborative marketing scenario involving five federated clients, each handling either real-world as Google Merchandise Store, UK Online Retail or synthetic as influencer and email campaign datasets. Through extensive experimentation, the framework demonstrates strong generalization across synthetic and real datasets, achieving classification accuracies up to 93.9%. Scalability tests across 5, 10, 15, and 20 clients validate the robustness of the aggregation method, with performance degradation remaining within 5%. The framework is benchmarked against baseline federated strategies such as FedAVG, FedSGD, and FedProx, and is evaluated under practical constraints using deployment-aligned analysis with frameworks like NVIDIA Clara, OpenFL, and Flower. This manuscript presents multiple comprehensive experimental analyses including convergence trends, client contribution evolution, resource utilization, and fairness-aware aggregation, making it a comprehensive study on privacy-preserving, ordinal, and adaptive federated analytics for modern digital marketing systems.