Strategies¶
nFL — No Federated Learning¶
| Name | Venue | Year | Paper | URL |
|---|---|---|---|---|
| LocalOnly | ||||
| Centralized | ||||
| SimTS | ICASSP | 2024 | SimTS: Rethinking Contrastive Representation Learning for Time Series Forecasting | Arxiv - IEEE - GitHub |
| InfoTS | ICLR | 2023 | InfoTS: Information-Aware Time Series Meta-Contrastive Learning | Arxiv - REF |
| SL | NeurIPS | 2025 | Selective Learning for Deep Time Series Forecasting | OpenReview - GitHub |
tFL — Traditional Federated Learning¶
| Name | Venue | Year | Paper | URL |
|---|---|---|---|---|
| FedAvg | AISTATS | 2017 | Communication-Efficient Learning of Deep Networks from Decentralized Data | Arxiv |
| SCAFFOLD | ICML | 2020 | SCAFFOLD: Stochastic Controlled Averaging for Federated Learning | Arxiv - REF |
| Krum | NeurIPS | 2017 | Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent | PUB - REF |
| FedMedian | ICML | 2018 | Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates | Arxiv - REF |
| FedTrimmedAvg | ICML | 2018 | Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates | Arxiv - REF |
| FedAdam | ICLR | 2021 | Adaptive Federated Optimization | Arxiv - REF |
| FedYogi | ICLR | 2021 | Adaptive Federated Optimization | Arxiv - REF |
| MOON | CVPR | 2021 | Model-Contrastive Federated Learning | Arxiv |
| FedRolex | NeurIPS | 2022 | FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extraction | Arxiv - GitHub |
| Elastic | CVPR | 2023 | Elastic Aggregation for Federated Optimization | OpenAccess - REF |
| FedCross | ICDE | 2024 | FedCross: Towards Accurate Federated Learning via Multi-Model Cross-Aggregation | IEEE - Arxiv - REF |
| FedRCL | CVPR | 2024 | Relaxed Contrastive Learning for Federated Learning | OpenAccess - Arxiv - GitHub |
| FedAWA | CVPR | 2025 | FedAWA: Adaptive Optimization of Aggregation Weights in Federated Learning Using Client Vectors | CVPR - Arxiv |
| FedTrend | Science China Information Sciences | 2026 | Tackling Data Heterogeneity in Federated Time Series Forecasting | PUB - Arxiv |
| FedAvgM | Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification | Arxiv - REF | ||
| FedADMM | ICDE | 2022 | FedADMM: A Robust Federated Deep Learning Framework with Adaptability to System Heterogeneity | IEEE - REF |
| FedLAW | ICML | 2023 | Revisiting Weighted Aggregation in Federated Learning with Neural Networks | Arxiv - GitHub |
| FedPAQ | AISTATS | 2020 | FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization | Arxiv |
pFL — Personalized Federated Learning¶
| Name | Venue | Year | Paper | URL |
|---|---|---|---|---|
| FedProx | MLsys | 2020 | Federated Optimization in Heterogeneous Networks | Arxiv |
| FedRidge | arXiv | 2026 | One-Shot Federated Ridge Regression: Exact Recovery via Sufficient Statistic Aggregation | Arxiv |
| Ditto | ICML | 2021 | Ditto: Fair and Robust Federated Learning Through Personalization | Arxiv - REF |
| pFedMe | NeurIPS | 2020 | Personalized Federated Learning with Moreau Envelopes | Arxiv - REF |
| APFL | arXiv | 2020 | Adaptive Personalized Federated Learning | Arxiv - REF |
| PerAvg | NeurIPS | 2020 | Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach | Arxiv - REF |
| AirMetapFL | arXiv | 2025 | Pre-Training and Personalized Fine-Tuning via Over-the-Air Federated Meta-Learning: Convergence-Generalization Trade-Offs | Arxiv |
| FedAMP | AAAI | 2021 | Personalized Cross-Silo Federated Learning on Non-IID Data | Arxiv - REF |
| FedBN | ICLR | 2021 | FedBN: Federated Learning on Non-IID Features via Local Batch Normalization | Arxiv - REF |
| FML | NeurIPS | 2020 | Federated Mutual Learning | Arxiv - REF |
| FedDyn | ICLR | 2021 | Federated Learning Based on Dynamic Regularization | Arxiv - REF |
| FedALA | AAAI | 2023 | FedALA: Adaptive Local Aggregation for Personalized Federated Learning | Arxiv |
| FedCAC | ICCV | 2023 | Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive Collaboration | OpenAccess - Arxiv |
| FDCR | NeurIPS | 2024 | Parameter Disparities Dissection for Backdoor Defense in Heterogeneous Federated Learning | Arxiv - GitHub |
| FedIT | ICASSP | 2024 | Towards Building the Federated GPT: Federated Instruction Tuning | Arxiv |
| FFA_LoRA | ICLR | 2024 | Improving LoRA in Privacy-preserving Federated Learning | Arxiv |
| FedSA_LoRA | ICLR | 2025 | Selective Aggregation for Low-Rank Adaptation in Federated Learning | Arxiv |
| LoRA_FAIR | ICCV | 2025 | LoRA-FAIR: Federated LoRA Fine-Tuning with Aggregation and Initialization Refinement | Arxiv - GitHub |
| pFedHN | ICML | 2021 | Personalized Federated Learning using Hypernetworks | Arxiv - GitHub |
| pFedLA | NeurIPS | 2022 | Layer-Wise Personalized Federated Learning via Hypernetworks | Arxiv - GitHub |
| CFL | arXiv | 2019 | Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints | Arxiv - REF |
| LGFedAvg | arXiv | 2020 | Think Locally, Act Globally: Federated Learning with Local and Global Representations | Arxiv - REF |
| FedNova | NeurIPS | 2020 | Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization | Arxiv - REF |
| FedSelect | CVPR | 2024 | FedSelect: Personalized Federated Learning with Customized Selection of Parameters for Fine-Tuning | Arxiv - GitHub |
hFL — Heterogeneous Federated Learning¶
| Name | Venue | Year | Paper | URL |
|---|---|---|---|---|
| FedMD | NeurIPS-W | 2019 | FedMD: Heterogenous Federated Learning via Model Distillation | Arxiv |
| FedDF* | NeurIPS | 2020 | Ensemble Distillation for Robust Model Fusion in Federated Learning | Arxiv |
dFL — Decentralized Federated Learning¶
| Name | Venue | Year | Paper | URL |
|---|---|---|---|---|
| DFedAvg** | AISTATS | 2017 | Communication-Efficient Learning of Deep Networks from Decentralized Data | Arxiv |
| DFedProx** | MLsys | 2020 | Federated Optimization in Heterogeneous Networks | Arxiv |
| DFedSAM | ICML | 2023 | Improving the Model Consistency of Decentralized Federated Learning | Arxiv - Slide |
| DFedAWA** | CVPR | 2025 | FedAWA: Adaptive Optimization of Aggregation Weights in Federated Learning Using Client Vectors | CVPR - Arxiv |
| DFedHPO | Internet of Things | 2025 | Decentralized Federated Learning with Hyperparameter Optimization | PUB |
* Adapted from classification to regression. Please use with caution.
** Decentralized variant converted from its tFL/pFL counterpart (e.g. FedAvg → DFedAvg).