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 37 篇，第 1 / 1 页

1. ICML 2026Accurate, Private, Secure, Federated U-statistics with Higher Degree
2. ICML 2026BESplit: Bias-Compensated Split Federated Learning with Evidential Aggregation
3. ICML 2026Breaking the Capacity Bottleneck in Model-Heterogeneous Federated Learning via Gradual Model Restoration
4. ICML 2026CLoVE: Personalized Federated Learning through Clustering of Loss Vector Embeddings
5. ICML 2026Controlled Collaboration Geometry for Personalized Federated Learning
6. ICML 2026Cross-task Calibration for Asynchronous Federated Continual Learning
7. ICML 2026Cross-View Lewis Weight Fusion Empowering Exemplar Replay for Federated Class-Incremental Learning
8. ICML 2026Explainable Federated Learning via Global–Local Attribution Alignment
9. ICML 2026FedEMoE: Improving Personalization on Heterogeneous Federated Learning via Elastic Mixture of Experts Architecture
10. ICML 2026Federated Bilevel Performative Prediction
11. ICML 2026Federated Graph Learning via Structure-Aware Fusion Using a Kalman Framework with Learnable Dynamics
12. ICML 2026Federated Multi-view Clustering for Remote Sensing Data
13. ICML 2026FedFit: Federated Dynamic Sparse Training via Fisher Information scoring
14. ICML 2026FedGain: Toward Negative-Gain-Free Client Collaboration in Federated Learning
15. ICML 2026FedHera: Towards Drift-Resilient Federated Fine-tuning with Heterogeneous Resources
16. ICML 2026FedHPro: Federated Hyper-Prototype Learning via Gradient Matching
17. ICML 2026FedQueue: Queue-Aware Federated Learning for Cross-Facility HPC Training
18. ICML 2026FedReLa: Imbalanced Federated Learning via Re-Labeling
19. ICML 2026FedRGL: Robust Federated Graph Learning under Label Noise
20. ICML 2026FedRot-LoRA: Mitigating Rotational Misalignment in Federated LoRA
21. ICML 2026FedScar: Correcting Geometric Bias for Flatness-Consistent Federated Learning
22. ICML 2026FedSDR: Federated Self-Distillation with Rectification
23. ICML 2026FedSSM: State Space Model-based Proactive Inference for Heterogeneous Multimodal Federated Learning
24. ICML 2026FedUSD: Unbiased Synthetic Data for Federated Learning
25. ICML 2026FedVeer: Self-Adaptive Skew Estimation for Robust Federated Learning
26. ICML 2026GeoEvo: Identity-Aware Potential Game with Geometric Evolution for Personalized Multimodal Federated Learning
27. ICML 2026Influence-Disentangled Federated Training: Learning Models That Are Easy to Unlearn
28. ICML 2026Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels
29. ICML 2026Lightweight Federated Incremental Learning via Decoupled Replay
30. ICML 2026One-Shot Weighted Ensemble Estimation for Federated Quantile Regression: Optimal Statistical Guarantees under Heterogeneous Structured Data
31. ICML 2026Prototype-guided Bilateral Alignment Multimodal Federated Learning
32. ICML 2026RC-FCL: Combating Asynchronous Concept Drift in Federated Continual Learning via Retrospective Calibration
33. ICML 2026SFedPO: Streaming Federated Learning with a Prediction Oracle under Temporal Shifts
34. ICML 2026ShapCCS: Shapley-Driven Client Coreset Selection in Federated Learning
35. ICML 2026Towards Rule-Based Knowledge Sharing in Federated Learning
36. ICML 2026Trustworthy Federated Label Distribution Learning under Annotation Quality Disparity
37. ICML 2026Weaving in the Clouds: Achieving Synergistic Collaboration among LLM Agents via 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。