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 113 篇，第 1 / 3 页

1. CVPR 2024An Aggregation-Free Federated Learning for Tackling Data Heterogeneity
2. CVPR 2024Communication-Efficient Federated Learning with Accelerated Client Gradient
3. CVPR 2024Decentralized Directed Collaboration for Personalized Federated Learning
4. CVPR 2024Device-Wise Federated Network Pruning
5. CVPR 2024FedAS: Bridging Inconsistency in Personalized Federated Learning
6. CVPR 2024Federated Generalized Category Discovery
7. CVPR 2024Federated Online Adaptation for Deep Stereo
8. CVPR 2024FedHCA2: Towards Hetero-Client Federated Multi-Task Learning
9. CVPR 2024FedMef: Towards Memory-efficient Federated Dynamic Pruning
10. CVPR 2024FedSelect: Personalized Federated Learning with Customized Selection of Parameters for Fine-Tuning
11. CVPR 2024FedSOL: Stabilized Orthogonal Learning with Proximal Restrictions in Federated Learning
12. CVPR 2024FLHetBench: Benchmarking Device and State Heterogeneity in Federated Learning
13. CVPR 2024Mixed-Precision Quantization for Federated Learning on Resource-Constrained Heterogeneous Devices
14. CVPR 2024Motion Blur Decomposition with Cross-shutter Guidance
15. CVPR 2024Relaxed Contrastive Learning for Federated Learning
16. CVPR 2024Rethinking the Representation in Federated Unsupervised Learning with Non-IID Data
17. CVPR 2024Revamping Federated Learning Security from a Defender's Perspective: A Unified Defense with Homomorphic Encrypted Data Space
18. CVPR 2024Text-Enhanced Data-free Approach for Federated Class-Incremental Learning
19. CVPR 2024Towards Efficient Replay in Federated Incremental Learning
20. CVPR 2024Traceable Federated Continual Learning
21. ICLR 2024A Lightweight Method for Tackling Unknown Participation Statistics in Federated Averaging
22. ICLR 2024Accurate Forgetting for Heterogeneous Federated Continual Learning
23. ICLR 2024Adaptive Federated Learning with Auto-Tuned Clients
24. ICLR 2024Backdoor Federated Learning by Poisoning Backdoor-Critical Layers
25. ICLR 2024Constructing Adversarial Examples for Vertical Federated Learning: Optimal Client Corruption through Multi-Armed Bandit
26. ICLR 2024FedCDA: Federated Learning with Cross-rounds Divergence-aware Aggregation
27. ICLR 2024FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices Using a Computing Power-Aware Scheduler
28. ICLR 2024Federated Orthogonal Training: Mitigating Global Catastrophic Forgetting in Continual Federated Learning
29. ICLR 2024Federated Recommendation with Additive Personalization
30. ICLR 2024Federated Wasserstein Distance
31. ICLR 2024FedInverse: Evaluating Privacy Leakage in Federated Learning
32. ICLR 2024FedTrans: Client-Transparent Utility Estimation for Robust Federated Learning
33. ICLR 2024FedWon: Triumphing Multi-domain Federated Learning Without Normalization
34. ICLR 2024Heterogeneous Personalized Federated Learning by Local-Global Updates Mixing via Convergence Rate
35. ICLR 2024Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learning
36. ICLR 2024Incentivized Truthful Communication for Federated Bandits
37. ICLR 2024Internal Cross-layer Gradients for Extending Homogeneity to Heterogeneity in Federated Learning
38. ICLR 2024Learning Personalized Causally Invariant Representations for Heterogeneous Federated Clients
39. ICLR 2024On Differentially Private Federated Linear Contextual Bandits
40. ICLR 2024PeFLL: Personalized Federated Learning by Learning to Learn
41. ICLR 2024Robust Training of Federated Models with Extremely Label Deficiency
42. ICLR 2024Stochastic Controlled Averaging for Federated Learning with Communication Compression
43. ICLR 2024Tackling the Data Heterogeneity in Asynchronous Federated Learning with Cached Update Calibration
44. ICML 2024A Federated Stochastic Multi-level Compositional Minimax Algorithm for Deep AUC Maximization
45. ICML 2024Accelerating Federated Learning with Quick Distributed Mean Estimation
46. ICML 2024Accelerating Heterogeneous Federated Learning with Closed-form Classifiers
47. ICML 2024Achieving Lossless Gradient Sparsification via Mapping to Alternative Space in Federated Learning
48. ICML 2024Adaptive Group Personalization for Federated Mutual Transfer Learning
49. ICML 2024AegisFL: Efficient and Flexible Privacy-Preserving Byzantine-Robust Cross-silo Federated Learning
50. ICML 2024Bridging Model Heterogeneity in Federated Learning via Uncertainty-based Asymmetrical Reciprocity 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。