Federated Learning Based Artificial Intelligence Systems with Blockchain Security for Global Healthcare Collaboration and Patient Centric Data Privacy

DOI

10.2991/978-94-6463-718-2_106How to use a DOI?

Keywords

FL; Blockchain security; worldwide healthcare collaboration; Patients Privacy-Data Privacy; Privacy-Preserving AI; Adversarial Attack Defense; Health Care; Smart Contracts; Scalable Healthcare AI, Differential Privacy; GDPR and HIPAA Compliance

Abstract

This study introduced a customer experience optimization framework built around AI, in which deep-network level sentiment and engagement ratings deliver a complete framework to optimize customer experience in real time transactions. The proposed system makes it possible to recognize customer emotions using multimodal sentiment classification techniques (e.g., speech emotion, facial emotion detection), as opposed to traditional text-based sentiment classifiers. By way of human-like AI interaction patterns employing transformer-based NLP models (BERT, GPT), reinforcement learning, and affective computing, adaptive engagements emulated human responses. Its capacity to monitor sentiment in real-time, noise-robust recognition of speech and ability to understand multilingual speech makes it especially beneficial for companies conducting business across international markets. → Innovative solutions then integrated seamlessly with the customers’ CRMs through different personas, allowing organizations to personalize each interaction, level of engagement based on sentiment, and satisfaction level. It also utilized Explainable AI (XAI) techniques to explain machine-level decisions or outcomes; ensuring that AI-powered decisions are more explainable and fairer, therefore ensuring that customer interactions through the framework remain transparent and ethical. The practical results demonstrated that the proposed system outperformed traditional sentiment analysis methods in terms of accuracy, adaptability, and real-time processing, leading to improved customer retention and engagement. The aforementioned issues such as computational resources challenges were discussed, and future research can optimize lightweight model and federated learning solutions for privacy-preserving sentiment analysis. In conclusion, this study presents as a germ of emotionally intelligent AI that can grow and attests the necessity for a shift of AI to a customer experience fine-tuned through adaptive, empathetic and humanistic interaction. This research contributes to the emerging field of AI-powered customer engagement, offering a springboard into bigger applications in a wide range of industries, including e-commerce, health care, financial services and retail.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - V. T. Krishnaprasath
AU  - Vamsee Pamisetty
AU  - Vikrant Sharma
AU  - Manjushree Nayak
AU  - N. N. Baalakumar
AU  - S. Aravindh
PY  - 2025
DA  - 2025/05/23
TI  - Federated Learning Based Artificial Intelligence Systems with Blockchain Security for Global Healthcare Collaboration and Patient Centric Data Privacy
BT  - Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
PB  - Atlantis Press
SP  - 1277
EP  - 1290
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-718-2_106
DO  - 10.2991/978-94-6463-718-2_106
ID  - Krishnaprasath2025
ER  -