Federated Learning(FL)has emerged as a transformative paradigm in machine learning,advocating for decentralized,privacy-preserving model training.This study provides a comprehensive evaluation of contemporary FL frame...Federated Learning(FL)has emerged as a transformative paradigm in machine learning,advocating for decentralized,privacy-preserving model training.This study provides a comprehensive evaluation of contemporary FL frameworks–TensorFlow Federated(TFF),PySyft,and FedJAX–across three diverse datasets:CIFAR-10,IMDb reviews,and the UCI Heart Disease dataset.Our results demonstrate TFF's superior performance on image classification tasks,while PySyft excels in both efficiency and privacy for textual data.The study underscores the potential of FL in ensuring data privacy and model performance,yet emphasizes areas warranting improvement.As the volume of edge devices escalates and the need for data privacy intensifies,refining and expanding FL frameworks become essential for future machine learning deployments.展开更多
文摘Federated Learning(FL)has emerged as a transformative paradigm in machine learning,advocating for decentralized,privacy-preserving model training.This study provides a comprehensive evaluation of contemporary FL frameworks–TensorFlow Federated(TFF),PySyft,and FedJAX–across three diverse datasets:CIFAR-10,IMDb reviews,and the UCI Heart Disease dataset.Our results demonstrate TFF's superior performance on image classification tasks,while PySyft excels in both efficiency and privacy for textual data.The study underscores the potential of FL in ensuring data privacy and model performance,yet emphasizes areas warranting improvement.As the volume of edge devices escalates and the need for data privacy intensifies,refining and expanding FL frameworks become essential for future machine learning deployments.
