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 29 篇，第 1 / 1 页

1. ICLR 2023Better Generative Replay for Continual Federated Learning
2. ICLR 2023Bias Propagation in Federated Learning
3. ICLR 2023Combating Exacerbated Heterogeneity for Robust Models in Federated Learning
4. ICLR 2023Data-Free One-Shot Federated Learning Under Very High Statistical Heterogeneity
5. ICLR 2023Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models
6. ICLR 2023DepthFL : Depthwise Federated Learning for Heterogeneous Clients
7. ICLR 2023Effective passive membership inference attacks in federated learning against overparameterized models
8. ICLR 2023Efficient Federated Domain Translation
9. ICLR 2023EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous Data
10. ICLR 2023FedDAR: Federated Domain-Aware Representation Learning
11. ICLR 2023Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach
12. ICLR 2023Federated Learning from Small Datasets
13. ICLR 2023Federated Nearest Neighbor Machine Translation
14. ICLR 2023Federated Neural Bandits
15. ICLR 2023FedExP: Speeding Up Federated Averaging via Extrapolation
16. ICLR 2023FedFA: Federated Feature Augmentation
17. ICLR 2023FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning
18. ICLR 2023Instance-wise Batch Label Restoration via Gradients in Federated Learning
19. ICLR 2023Learning to CROSS exchange to solve min-max vehicle routing problems
20. ICLR 2023Machine Unlearning of Federated Clusters
21. ICLR 2023Meta Knowledge Condensation for Federated Learning
22. ICLR 2023MocoSFL: enabling cross-client collaborative self-supervised learning
23. ICLR 2023Multimodal Federated Learning via Contrastive Representation Ensemble
24. ICLR 2023PerFedMask: Personalized Federated Learning with Optimized Masking Vectors
25. ICLR 2023Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses
26. ICLR 2023SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication
27. ICLR 2023Test-Time Robust Personalization for Federated Learning
28. ICLR 2023Towards Addressing Label Skews in One-Shot Federated Learning
29. ICLR 2023Turning the Curse of Heterogeneity in Federated Learning into a Blessing for Out-of-Distribution Detection

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。