PFLlib

Algorithms With Code (Updating)

We categorize FL algorithms into 8 tFL algorithms and 29 pFL algorithms based on their foundational techniques. The detailed classification is outlined below.

Traditional FL (tFL)

• Basic tFL
• FedAvg — Communication-Efficient Learning of Deep Networks from Decentralized Data AISTATS 2017
• Update-correction-based tFL
• SCAFFOLD - SCAFFOLD: Stochastic Controlled Averaging for Federated Learning ICML 2020
• Regularization-based tFL
• FedProx — Federated Optimization in Heterogeneous Networks MLsys 2020
• FedDyn — Federated Learning Based on Dynamic Regularization ICLR 2021
• Model-splitting-based tFL
• MOON — Model-Contrastive Federated Learning CVPR 2021
• FedLC — Federated Learning With Label Distribution Skew via Logits Calibration ICML 2022
• Knowledge-distillation-based tFL
• FedGen — Data-Free Knowledge Distillation for Heterogeneous Federated Learning ICML 2021
• FedNTD — Preservation of the Global Knowledge by Not-True Distillation in Federated Learning NeurIPS 2022

Personalized FL (pFL)

• Meta-learning-based pFL
• Per-FedAvg — Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach NeurIPS 2020
• Regularization-based pFL
• pFedMe — Personalized Federated Learning with Moreau Envelopes NeurIPS 2020
• Ditto — Ditto: Fair and robust federated learning through personalization ICML 2021
• Personalized-aggregation-based pFL
• APFL — Adaptive Personalized Federated Learning 2020
• FedFomo — Personalized Federated Learning with First Order Model Optimization ICLR 2021
• FedAMP — Personalized Cross-Silo Federated Learning on non-IID Data AAAI 2021
• FedPHP — FedPHP: Federated Personalization with Inherited Private Models ECML PKDD 2021
• APPLE — Adapt to Adaptation: Learning Personalization for Cross-Silo Federated Learning IJCAI 2022
• FedALA — FedALA: Adaptive Local Aggregation for Personalized Federated Learning AAAI 2023
• Model-splitting-based pFL
• FedPer — Federated Learning with Personalization Layers 2019
• LG-FedAvg — Think Locally, Act Globally: Federated Learning with Local and Global Representations 2020
• FedRep — Exploiting Shared Representations for Personalized Federated Learning ICML 2021
• FedRoD — On Bridging Generic and Personalized Federated Learning for Image Classification ICLR 2022
• FedBABU — Fedbabu: Towards enhanced representation for federated image classification ICLR 2022
• FedGC — Federated Learning for Face Recognition with Gradient Correction AAAI 2022
• FedCP — FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policy KDD 2023
• GPFL — GPFL: Simultaneously Learning Generic and Personalized Feature Information for Personalized Federated Learning ICCV 2023
• FedGH — FedGH: Heterogeneous Federated Learning with Generalized Global Header ACM MM 2023
• FedDBE — Eliminating Domain Bias for Federated Learning in Representation Space NeurIPS 2023
• FedCAC — Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive Collaboration ICCV 2023
• PFL-DA — Personalized Federated Learning via Domain Adaptation with an Application to Distributed 3D Printing Technometrics 2023
• Knowledge-distillation-based pFL (more in HtFLlib)
• FedDistill (FD) — Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data 2018
• FML — Federated Mutual Learning 2020
• FedKD — Communication-efficient federated learning via knowledge distillation Nature Communications 2022
• FedProto — FedProto: Federated Prototype Learning across Heterogeneous Clients AAAI 2022
• FedPCL (w/o pre-trained models) — Federated learning from pre-trained models: A contrastive learning approach NeurIPS 2022
• FedPAC — Personalized Federated Learning with Feature Alignment and Classifier Collaboration ICLR 2023
• Other pFL
• FedMTL (not MOCHA) — Federated multi-task learning NeurIPS 2017
• FedBN — FedBN: Federated Learning on non-IID Features via Local Batch Normalization ICLR 2021