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Federated learning (FL) technology in recommendation systems generates many possibilities together with technical obstacles that need addressing for future growth. Three main research areas lie in creating federated deep learning methods and graph models for recommendations and rein-enforcement learning mechanisms for dynamic choices and recall-based and ranking-based framework definitions. FL effectiveness in recommendation systems depends on resolving the issues of malicious cooperation and non-IID data distribution and optimizing flexibility and scalability as well as communication costs. Failure to follow prescribed medications creates substantial healthcare difficulties in the domain which diminishes treatment effectiveness and leads to worse health outcomes and elevated hospitalization rates and mortality statistics. Barriers exist for healthcare providers to adopt the medication administration tools which include intelligent drug administration systems (IDAS) and smart blister packs for improving medication adherence rates. The training process of federated learning utilizes decentralized Smart Pill Case data to provide users with upgraded medication suggestion services and notification systems. The approach enables better medicine adherence programs and privacy protection through holistic data security that upholds privacy confidentiality.

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Advancing Federated Learning for Privacy-Preserving Recommendation Systems in Healthcare: Enhancing Medication Adherence with Smart Pill Case Integration

  • 会议论文

  • 发表日期：

    2025年

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  • Gaurav Goel,
  • Anil Kumar Pandey,
  • Dinesh Kumar Singh

摘要

Federated learning (FL) technology in recommendation systems generates many possibilities together with technical obstacles that need addressing for future growth. Three main research areas lie in creating federated deep learning methods and graph models for recommendations and rein-enforcement learning mechanisms for dynamic choices and recall-based and ranking-based framework definitions. FL effectiveness in recommendation systems depends on resolving the issues of malicious cooperation and non-IID data distribution and optimizing flexibility and scalability as well as communication costs. Failure to follow prescribed medications creates substantial healthcare difficulties in the domain which diminishes treatment effectiveness and leads to worse health outcomes and elevated hospitalization rates and mortality statistics. Barriers exist for healthcare providers to adopt the medication administration tools which include intelligent drug administration systems (IDAS) and smart blister packs for improving medication adherence rates. The training process of federated learning utilizes decentralized Smart Pill Case data to provide users with upgraded medication suggestion services and notification systems. The approach enables better medicine adherence programs and privacy protection through holistic data security that upholds privacy confidentiality.