International Journal on Science and Technology

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Use of Federated Learning for Optimizing Ad Delivery Platforms without Exchanging User PII

Author(s) Varun Chivukula
Country United States
Abstract The ever-expanding digital advertising ecosystem relies heavily on advanced machine learning (ML) models to predict user behavior, personalize content, and optimize ad delivery. However, traditional centralized ML workflows that aggregate and process large amounts of Personally Identifiable Information (PII) are increasingly incompatible with growing regulatory constraints such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). Federated Learning (FL) provides a revolutionary approach by enabling decentralized model training across distributed data sources while ensuring that raw user data never leaves local environments.
This paper explores the implementation of FL in ad delivery platforms to train ML models optimized for driving conversions while preserving user privacy. It discusses the architecture, benefits, and challenges of FL in the context of ad delivery, using practical examples to illustrate its effectiveness. Furthermore, it delves into future innovations, such as integrating FL with complementary technologies like transfer learning, secure aggregation, and multi-party computation, to address data heterogeneity and improve scalability. By leveraging FL, ad platforms can balance the dual goals of delivering personalized user experiences and maintaining compliance with stringent privacy regulations.
Keywords Federated Learning, Machine Learning, Privacy-Preserving Technologies, Digital Advertising, Conversion Optimization, Ad Delivery Platforms, Data Privacy, Regulatory Compliance
Published In Volume 13, Issue 3, July-September 2022
Published On 2022-07-05
Cite This Use of Federated Learning for Optimizing Ad Delivery Platforms without Exchanging User PII - Varun Chivukula - IJSAT Volume 13, Issue 3, July-September 2022. DOI 10.5281/zenodo.14613805
DOI https://doi.org/10.5281/zenodo.14613805
Short DOI https://doi.org/g8x2wj
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