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Data privacy regulations, such as the General Data Protection Regulation (GDPR), have significantly impacted how organizations handle and process personal data. Compliance with GDPR requires organizations to protect individuals’ privacy rights while still harnessing the benefits of data-driven decision-making. Federated Learning, a novel machine learning approach, offers a promising solution by enabling collaborative model training across decentralized data sources while preserving data privacy. This research paper delves into the intricacies of Federated Learning and its role in achieving GDPR compliance, highlighting its potential advantages, challenges, and future prospects.

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Federated Learning for GDPR Compliance

  • 会议论文

  • 发表日期：

    2025年

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  • Suyogita Singh,
  • Satya Bhushan Verma,
  • Bineet Kumar Gupta,
  • Monika Gupta,
  • Sandeep Dubey

摘要

Data privacy regulations, such as the General Data Protection Regulation (GDPR), have significantly impacted how organizations handle and process personal data. Compliance with GDPR requires organizations to protect individuals’ privacy rights while still harnessing the benefits of data-driven decision-making. Federated Learning, a novel machine learning approach, offers a promising solution by enabling collaborative model training across decentralized data sources while preserving data privacy. This research paper delves into the intricacies of Federated Learning and its role in achieving GDPR compliance, highlighting its potential advantages, challenges, and future prospects.