PFLlib: Personalized Federated Learning Algorithm Library
Jianqing Zhang Yang Liu Yang Hua Hao Wang Tao Song Zhengui Xue Ruhui Ma Jian Cao
Amid the ongoing advancements in Federated Learning (FL), a machine learning
paradigm that allows collaborative learning with data privacy protection,
personalized FL (pFL) has gained significant prominence as a research direction
within the FL domain. Whereas traditional FL (tFL) focuses on jointly learning
a global model, pFL aims to achieve a balance between the global and
personalized objectives of each client in FL settings. To foster the pFL
research community, we propose PFLlib, a comprehensive pFL algorithm library
with an integrated evaluation platform. In PFLlib, We implement 34
state-of-the-art FL algorithms (including 7 classic tFL algorithms and 27 pFL
algorithms) and provide various evaluation environments with three
statistically heterogeneous scenarios and 14 datasets. At present, PFLlib has
already gained 850 stars and 199 forks on GitHub.