联邦学习论文趋势 2023–2026

主要结论

• 联邦学习论文从 2023 年的 137 篇降到 2026 年的 119 篇，热度在回落。
• NeurIPS 是发联邦学习论文最多的会议，2023–2026 年共 157 篇，占四个会议总量的 34%。
• 交叉方向里涨得最快的是低秩适配：同时属于两个主题的论文从 2023 年的 0 篇增至 2026 年的 12 篇。
• 在「联邦与隐私」的 5 个主题里，它的 2026 年论文数排第 2；同组增长最快的安全聚合是 1.8 倍，联邦学习是 0.9 倍。

代表论文

按年份和会议均匀挑选，方便快速看到每一届的样貌

• NeurIPS 2026A typed tensor language for federated learning
• ICML 2026BESplit: Bias-Compensated Split Federated Learning with Evidential Aggregation
• ICLR 2026Beyond Aggregation: Guiding Clients in Heterogeneous Federated Learning
• CVPR 2026FedRG Unleashing the Representation Geometry for Federated Learning with Noisy
• NeurIPS 2025Class-wise Balancing Data Replay for Federated Class-Incremental Learning
• ICML 2025Causality Inspired Federated Learning for OOD Generalization
• ICLR 2025Adaptive Gradient Clipping for Robust Federated Learning
• CVPR 2025FedCS: Coreset Selection for Federated Learning
• NeurIPS 2024Local Superior Soups: A Catalyst for Model Merging in Cross-Silo Federated Learning
• ICML 2024Achieving Lossless Gradient Sparsification via Mapping to Alternative Space in Federated Learning
• ICLR 2024FedTrans: Client-Transparent Utility Estimation for Robust Federated Learning
• CVPR 2024Decentralized Directed Collaboration for Personalized Federated Learning
• NeurIPS 2023DELTA: Diverse Client Sampling for Fasting Federated Learning
• ICML 2023Achieving Linear Speedup in Non-IID Federated Bilevel Learning
• ICLR 2023Combating Exacerbated Heterogeneity for Robust Models in Federated Learning
• CVPR 2023Federated Domain Generalization With Generalization Adjustment
• NeurIPS 2026Consistency-Verified Backdoor Defense for Federated Graph Learning via Cross-Layer Drift
• ICML 2026Breaking the Capacity Bottleneck in Model-Heterogeneous Federated Learning via Gradual Model Restoration
• ICLR 2026DeepAFL: Deep Analytic Federated Learning
• CVPR 2026FedSST Rethinking Fair Federated Graph Learning under Structural Shift

全部联邦学习论文

共 80 篇，第 1 / 2 页

1. CVPR 2026Batman Benign Knowledge Alignment Through Malicious Null Space in Federated
2. CVPR 2026Controllable Federated Prompt Learning at Test Time
3. CVPR 2026Domain Sensitive Federated Learning with Fisher-Informed Pruning
4. CVPR 2026DP-FedAdamW An Efficient Optimizer for Differentially Private Federated Large Models
5. CVPR 2026FedAlign Differentially Private Distribution Alignment for Non-IID Federated Learning
6. CVPR 2026FedBPrompt Federated Domain Generalization Person Re-Identification via Body Distribution Aware
7. CVPR 2026FedCART Tackling Long-Tailed Distributions in Federated Adversarial Training via Classifier
8. CVPR 2026FedDAP Domain-Aware Prototype Learning for Federated Learning under Domain Shift
9. CVPR 2026FedHarmony Harmonizing Heterogeneous Label Correlations in Federated Multi-Label Learning
10. CVPR 2026FedRAC Rolling Submodel Allocation for Collaborative Fairness in Federated Learning
11. CVPR 2026FedRE A Representation Entanglement Framework for Model-Heterogeneous Federated Learning
12. CVPR 2026FedRG Unleashing the Representation Geometry for Federated Learning with Noisy
13. CVPR 2026FedSST Rethinking Fair Federated Graph Learning under Structural Shift
14. CVPR 2026Few-for-Many Personalized Federated Learning
15. CVPR 2026From Selection to Scheduling Federated Geometry-Aware Correction Makes Exemplar Replay
16. CVPR 2026GDFA Geometry-Driven Federated Unlearning with Directional Task Vector Alignment
17. CVPR 2026HiLoRA Hierarchical Low-Rank Adaptation for Personalized Federated Learning
18. CVPR 2026Pose-guided Enriched Feature Learning for Federated-by-camera Person Re-identification
19. CVPR 2026ProxyFL A Proxy-Guided Framework for Federated Semi-Supervised Learning
20. CVPR 2026RHO Robust Holistic OSM-Based Metric Cross-View Geo-Localization
21. CVPR 2026Single-Round Scalable Analytic Federated Learning
22. CVPR 2025A Simple Data Augmentation for Feature Distribution Skewed Federated Learning
23. CVPR 2025AFL: A Single-Round Analytic Approach for Federated Learning with Pre-trained Models
24. CVPR 2025CroCoDL: Cross-device Collaborative Dataset for Localization
25. CVPR 2025dFLMoE: Decentralized Federated Learning via Mixture of Experts for Medical Data Analysis
26. CVPR 2025FedAWA: Adaptive Optimization of Aggregation Weights in Federated Learning Using Client Vectors
27. CVPR 2025FedCALM: Conflict-aware Layer-wise Mitigation for Selective Aggregation in Deeper Personalized Federated Learning
28. CVPR 2025FedCS: Coreset Selection for Federated Learning
29. CVPR 2025Federated Learning with Domain Shift Eraser
30. CVPR 2025FedMIA: An Effective Membership Inference Attack Exploiting "All for One" Principle in Federated Learning
31. CVPR 2025FedSPA: Generalizable Federated Graph Learning under Homophily Heterogeneity
32. CVPR 2025Geometric Knowledge-Guided Localized Global Distribution Alignment for Federated Learning
33. CVPR 2025Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor
34. CVPR 2025HistoFS: Non-IID Histopathologic Whole Slide Image Classification via Federated Style Transfer with RoI-Preserving
35. CVPR 2025Infighting in the Dark: Multi-Label Backdoor Attack in Federated Learning
36. CVPR 2025Mind the Gap: Confidence Discrepancy Can Guide Federated Semi-Supervised Learning Across Pseudo-Mismatch
37. CVPR 2025NoT: Federated Unlearning via Weight Negation
38. CVPR 2025pFedMxF: Personalized Federated Class-Incremental Learning with Mixture of Frequency Aggregation
39. CVPR 2025Population Normalization for Federated Learning
40. CVPR 2025Unlearning through Knowledge Overwriting: Reversible Federated Unlearning via Selective Sparse Adapter
41. CVPR 2024An Aggregation-Free Federated Learning for Tackling Data Heterogeneity
42. CVPR 2024Communication-Efficient Federated Learning with Accelerated Client Gradient
43. CVPR 2024Decentralized Directed Collaboration for Personalized Federated Learning
44. CVPR 2024Device-Wise Federated Network Pruning
45. CVPR 2024FedAS: Bridging Inconsistency in Personalized Federated Learning
46. CVPR 2024Federated Generalized Category Discovery
47. CVPR 2024Federated Online Adaptation for Deep Stereo
48. CVPR 2024FedHCA2: Towards Hetero-Client Federated Multi-Task Learning
49. CVPR 2024FedMef: Towards Memory-efficient Federated Dynamic Pruning
50. CVPR 2024FedSelect: Personalized Federated Learning with Customized Selection of Parameters for Fine-Tuning

常见问题

2023–2026 年四大会议一共有多少篇联邦学习论文？

共 460 篇，按年份分别是 2023 年 137 篇、2024 年 113 篇、2025 年 91 篇、2026 年 119 篇。

NeurIPS 2026 有多少篇联邦学习论文？

NeurIPS 2026 有 42 篇联邦学习论文，上一届是 31 篇。

ICLR 2026 有多少篇联邦学习论文？

ICLR 2026 有 19 篇联邦学习论文，上一届是 18 篇。

ICML 2026 有多少篇联邦学习论文？

ICML 2026 有 37 篇联邦学习论文，上一届是 23 篇。

CVPR 2026 有多少篇联邦学习论文？

CVPR 2026 有 21 篇联邦学习论文，上一届是 19 篇。

哪个会议的联邦学习论文最多？

NeurIPS，2023–2026 年共 157 篇。