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.

翻译：随着联邦学习（FL）这一支持数据隐私保护下的协同学习的机器学习范式的持续发展，个性化联邦学习（pFL）已成为FL领域内一个重要的研究方向。传统联邦学习（tFL）侧重于联合学习全局模型，而pFL则致力于在FL场景中平衡每个客户的全局与个性化目标。为促进pFL研究社区的发展，我们提出了PFLlib，一个集评估平台于一体的综合性pFL算法库。在PFLlib中，我们实现了34种先进的FL算法（包括7种经典tFL算法和27种pFL算法），并提供了包含三种统计异构场景和14个数据集的多种评估环境。目前，PFLlib在GitHub上已获得850颗星标和199次分叉。