• Jamilu Habu
• Babangida S Imam
• Zainab Mukhtar Sani
• Bachcha Lal Pal
• Ajay Singh Dhabariya

The integration of machine learning (ML) into healthcare has significantly enhanced patient care and operational efficiency. However, the use of sensitive patient data raises serious privacy concerns. Federated learning (FL) has emerged as a promising approach to address these concerns by enabling the training of ML models across decentralized data sources without the need to share raw data. This review paper provides a comprehensive examination of privacy-preserving federated learning in healthcare. It discusses the fundamental principles of FL, various privacy-preserving techniques such as differential privacy, secure multiparty computation, and homomorphic encryption, and their applications in healthcare. The paper also evaluates current FL implementations in healthcare, highlighting their effectiveness and challenges. Furthermore, it identifies future research opportunities and advancements needed to overcome existing limitations. By safeguarding patient data while maintaining high model performance, privacy-preserving federated learning has the potential to revolutionize the healthcare industry. This review aims to provide valuable insights for healthcare organizations and researchers interested in implementing federated learning solutions responsibly.

Federated Learning, Machine Learning, Healthcare, Privacy preserving techniques, Differential Privacy, Secure Multiparty computation, Homomorphic Encryption, Decentralized Data source, Patient Data.

"PRIVACY PRESERVING FEDERATED LEARNING IN HEALTHCARE: A COMPREHENSIVE REVIEW", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.K75-K80, June-2024, Available :http://www.jetir.org/papers/JETIR2406A09.pdf

"PRIVACY PRESERVING FEDERATED LEARNING IN HEALTHCARE: A COMPREHENSIVE REVIEW", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. ppK75-K80, June-2024, Available at : http://www.jetir.org/papers/JETIR2406A09.pdf