We create a beginner-friendly library with a evaluation platform for those new to federated learning (FL). Join us in benefiting the FL community by contributing your algorithms, datasets, and metrics to this project.
+We create a beginner-friendly library with an evaluation platform for those new to federated learning (FL). Join us in benefiting the FL community by contributing your algorithms, datasets, and metrics to this project.
PFLlib is ideal for companies aiming to explore, select, and evaluate personalized federated learning methods. It enables the evaluation of algorithms and their adaptability to diverse scenarios, offering valuable insights for informed algorithm selection in real-world applications. With its robust features, PFLlib is well-suited for a wide range of industries, from healthcare to finance.
+PFLlib is ideal for companies aiming to explore, select, and evaluate standard/personalized federated learning methods. It enables the evaluation of algorithms and their adaptability to diverse scenarios, offering valuable insights for informed algorithm selection in real-world applications. With its robust features, PFLlib is well-suited for a wide range of industries, from healthcare to finance.
I am Jianqing Zhang, a PhD student at Shanghai Jiao Tong University and an active contributor to the federated learning community. My research centers on pioneering personalized and scalable solutions for cloud-edge collaboration and federated learning, driving innovation in this transformative field. Explore my homepage for more details about my research and projects.
Together, let's shape the future of federated learning and make it even greater!