Advances in Privacy Preserving Federated Learning to Realize a Truly Learning Healthcare System
<|reference_start|>Advances in Privacy Preserving Federated Learning to Realize a Truly Learning Healthcare System: The concept of a learning healthcare system (LHS) envisions a self-improving network where multimodal data from patient care are continuously analyzed to enhance future healthcare outcomes. However, realizing this vision faces significant challenges in data sharing and privacy protection. Privacy-Preserving Federated Learning (PPFL) is a transformative and promising approach that has the potential to address these challenges by enabling collaborative learning from decentralized data while safeguarding patient privacy. This paper proposes a vision for integrating PPFL into the healthcare ecosystem to achieve a truly LHS as defined by the Institute of Medicine (IOM) Roundtable.<|reference_end|>
@article{madduri2024advances,
    title={Advances in Privacy Preserving Federated Learning to Realize a Truly
  Learning Healthcare System},
    author={Ravi Madduri, Zilinghan Li, Tarak Nandi, Kibaek Kim, Minseok Ryu, Alex
  Rodriguez},
    journal={arXiv preprint arXiv:2409.19756},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.19756},
    primaryClass={cs.CR cs.DC}
}