Research map / Federated learning and privacy
Personalized and heterogeneous FL: research map
738 accepted papers on Personalized and heterogeneous FL in Federated learning and privacy, from ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026), grouped into 2 clusters and 9 approaches. The busiest year so far is 2026.
Within Federated learning and privacy, its share grew from 33.5% in 2023–24 to 42.6% in 2025–26 (249 → 389 papers at ICML, NeurIPS, CVPR and AAAI, the venues with data for all four years).
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Approaches and key papers
domain · label · multi · 619 papers
Approaches in this cluster:
- Non-IID federated learning (198 papers)
Counter data heterogeneity with drift decoupling, distillation and re-labeling. - Federated learning with pretrained models (164 papers)
Handle client imbalance and long-tailed data with pretrained and CLIP models and consistency. - Federated semi-supervised and graph learning (84 papers)
Learn from unlabeled or graph data across clients with pseudo-labeling. - Federated prompt and LoRA tuning (91 papers)
Adapt foundation models federatedly with prompts and low-rank adapters. - Federated medical and multimodal learning (44 papers)
Harmonize heterogeneous institutions and modalities, including medical imaging domains. - Vertical federated learning (38 papers)
Train across parties holding different features with privacy and communication efficiency.
Most cited and most cited since 2024:
- Model-Contrastive Federated Learning (CVPR 2021 · 1,518 citations)
- FedProto: Federated Prototype Learning across Heterogeneous Clients (AAAI 2022 · 751 citations)
- FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated Learning (AAAI 2024 · 114 citations)
- FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated Learning (AAAI 2024 · 82 citations)
personalized federated · pfl · personalization · 119 papers
Approaches in this cluster:
- Personalization with global-local trade-off (48 papers)
Balance global and personalized federated models with sharpness, prototypes and Gaussian processes. - Sparse and collaboration-graph personalization (36 papers)
Adapt models to clients via sparse adaptation, latent constraints and inferred collaboration graphs. - Bayesian personalized FL (35 papers)
Personalize federated models with Bayesian inference, model reassembly and self-awareness.
Most cited and most cited since 2024:
- FedALA: Adaptive Local Aggregation for Personalized Federated Learning (AAAI 2023 · 414 citations)
- Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach (NeurIPS 2020 · 373 citations)
- FedAS: Bridging Inconsistency in Personalized Federated Learning (CVPR 2024 · 99 citations)
- FedSelect: Personalized Federated Learning with Customized Selection of Parameters for Fine-Tuning (CVPR 2024 · 38 citations)