Facilitating large-scale, cross-institutional collaboration in biomedical machine learning projects requires a trustworthy and resilient federated learning (FL) environment to ensure that sensitive information such as protected health information is kept confidential. In this work, we introduce APPFLx, a low-code FL framework that enables the easy setup, configuration, and running of FL experiments across organizational and administrative boundaries while providing secure end-to-end communication, privacy-preserving functionality, and identity management. APPFLx is completely agnostic to the underlying computational infrastructure of participating clients. We demonstrate the capability of APPFLx as an easy-to-use framework for accelerating biomedical studies across institutions and healthcare systems while maintaining the protection of private medical data in two case studies: (1) predicting participant age from electrocardiogram (ECG) waveforms, and (2) detecting COVID-19 disease from chest radiographs. These experiments were performed securely across heterogeneous compute resources, including a mixture of on-premise high-performance computing and cloud computing, and highlight the role of federated learning in improving model generalizability and performance when aggregating data from multiple healthcare systems. Finally, we demonstrate that APPFLx serves as a convenient and easy-to-use framework for accelerating biomedical studies across institutions and healthcare system while maintaining the protection of private medical data.

翻译：推动大规模跨机构生物医学机器学习项目合作，需要建立可信且鲁棒的联邦学习环境，以确保受保护的健康信息等敏感数据的机密性。本研究提出APPFLx——一个低代码联邦学习框架，支持跨组织与管理边界轻松完成联邦学习实验的配置与部署，同时提供安全的端到端通信、隐私保护功能及身份管理机制。APPFLx完全独立于参与客户端的底层计算基础设施。通过两个案例研究，我们展示了APPFLx作为易用框架在加速跨机构与医疗系统生物医学研究的同时保护私有医疗数据的能力：（1）根据心电图波形预测参与者年龄；（2）通过胸部X光片检测COVID-19疾病。这些实验在包含本地高性能计算与云计算混合环境的异构计算资源中安全执行，突显了联邦学习在聚合多医疗系统数据时提升模型泛化性能与表现的关键作用。最后，我们证明APPFLx是一个便捷易用的框架，能在保护私有医疗数据的同时加速跨机构与医疗系统的生物医学研究。