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 91 篇，第 1 / 2 页

1. CVPR 2025A Simple Data Augmentation for Feature Distribution Skewed Federated Learning
2. CVPR 2025AFL: A Single-Round Analytic Approach for Federated Learning with Pre-trained Models
3. CVPR 2025CroCoDL: Cross-device Collaborative Dataset for Localization
4. CVPR 2025dFLMoE: Decentralized Federated Learning via Mixture of Experts for Medical Data Analysis
5. CVPR 2025FedAWA: Adaptive Optimization of Aggregation Weights in Federated Learning Using Client Vectors
6. CVPR 2025FedCALM: Conflict-aware Layer-wise Mitigation for Selective Aggregation in Deeper Personalized Federated Learning
7. CVPR 2025FedCS: Coreset Selection for Federated Learning
8. CVPR 2025Federated Learning with Domain Shift Eraser
9. CVPR 2025FedMIA: An Effective Membership Inference Attack Exploiting "All for One" Principle in Federated Learning
10. CVPR 2025FedSPA: Generalizable Federated Graph Learning under Homophily Heterogeneity
11. CVPR 2025Geometric Knowledge-Guided Localized Global Distribution Alignment for Federated Learning
12. CVPR 2025Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor
13. CVPR 2025HistoFS: Non-IID Histopathologic Whole Slide Image Classification via Federated Style Transfer with RoI-Preserving
14. CVPR 2025Infighting in the Dark: Multi-Label Backdoor Attack in Federated Learning
15. CVPR 2025Mind the Gap: Confidence Discrepancy Can Guide Federated Semi-Supervised Learning Across Pseudo-Mismatch
16. CVPR 2025NoT: Federated Unlearning via Weight Negation
17. CVPR 2025pFedMxF: Personalized Federated Class-Incremental Learning with Mixture of Frequency Aggregation
18. CVPR 2025Population Normalization for Federated Learning
19. CVPR 2025Unlearning through Knowledge Overwriting: Reversible Federated Unlearning via Selective Sparse Adapter
20. ICLR 2025Adaptive Gradient Clipping for Robust Federated Learning
21. ICLR 2025Can Textual Gradient Work in Federated Learning?
22. ICLR 2025Connecting Federated ADMM to Bayes
23. ICLR 2025Debiasing Federated Learning with Correlated Client Participation
24. ICLR 2025Decentralized Sporadic Federated Learning: A Unified Algorithmic Framework with Convergence Guarantees
25. ICLR 2025Decoupled Subgraph Federated Learning
26. ICLR 2025Event-Driven Online Vertical Federated Learning
27. ICLR 2025Federated Domain Generalization with Data-free On-server Matching Gradient
28. ICLR 2025Federated Few-Shot Class-Incremental Learning
29. ICLR 2025Federated Residual Low-Rank Adaptation of Large Language Models
30. ICLR 2025FedLWS: Federated Learning with Adaptive Layer-wise Weight Shrinking
31. ICLR 2025FedTMOS: Efficient One-Shot Federated Learning with Tsetlin Machine
32. ICLR 2025On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations
33. ICLR 2025Problem-Parameter-Free Federated Learning
34. ICLR 2025SparsyFed: Sparse Adaptive Federated Learning
35. ICLR 2025Subgraph Federated Learning for Local Generalization
36. ICLR 2025Towards Federated RLHF with Aggregated Client Preference for LLMs
37. ICLR 2025Unlocking the Potential of Model Calibration in Federated Learning
38. ICML 2025Causality Inspired Federated Learning for OOD Generalization
39. ICML 2025FedBEns: One-Shot Federated Learning based on Bayesian Ensemble
40. ICML 2025FedECADO: A Dynamical System Model of Federated Learning
41. ICML 2025Federated Generalised Variational Inference: A Robust Probabilistic Federated Learning Framework
42. ICML 2025Federated In-Context Learning: Iterative Refinement for Improved Answer Quality
43. ICML 2025Federated Learning for Feature Generalization with Convex Constraints
44. ICML 2025Federated Node-Level Clustering Network with Cross-Subgraph Link Mending
45. ICML 2025FedSSI: Rehearsal-Free Continual Federated Learning with Synergistic Synaptic Intelligence
46. ICML 2025Ferret: Federated Full-Parameter Tuning at Scale for Large Language Models
47. ICML 2025FSL-SAGE: Accelerating Federated Split Learning via Smashed Activation Gradient Estimation
48. ICML 2025Gap-Dependent Bounds for Federated $Q$-Learning
49. ICML 2025Harnessing Heterogeneous Statistical Strength for Personalized Federated Learning via Hierarchical Bayesian Inference
50. ICML 2025Hybrid Batch Normalisation: Resolving the Dilemma of Batch Normalisation in Federated Learning

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。