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 31 篇，第 1 / 1 页

1. NeurIPS 2025A Fair Federated Learning Method for Handling Client Participation Probability Inconsistencies in Heterogeneous Environments
2. NeurIPS 2025A Reinforcement Learning-based Bidding Strategy for Data Consumers in Auction-based Federated Learning
3. NeurIPS 2025Accelerated Vertical Federated Adversarial Learning through Decoupling Layer-Wise Dependencies
4. NeurIPS 2025Adaptive Latent-Space Constraints in Personalized Federated Learning
5. NeurIPS 2025Class-wise Balancing Data Replay for Federated Class-Incremental Learning
6. NeurIPS 2025Competitive Advantage Attacks to Decentralized Federated Learning
7. NeurIPS 2025Covariances for Free: Exploiting Mean Distributions for Training-free Federated Learning
8. NeurIPS 2025Decentralized Dynamic Cooperation of Personalized Models for Federated Continual Learning
9. NeurIPS 2025Feature Distillation is the Better Choice for Model-Heterogeneous Federated Learning
10. NeurIPS 2025FedEL: Federated Elastic Learning for Heterogeneous Devices
11. NeurIPS 2025FedGPS: Statistical Rectification Against Data Heterogeneity in Federated Learning
12. NeurIPS 2025FedIGL: Federated Invariant Graph Learning for Non-IID Graphs
13. NeurIPS 2025FedRTS: Federated Robust Pruning via Combinatorial Thompson Sampling
14. NeurIPS 2025FlowerTune: A Cross-Domain Benchmark for Federated Fine-Tuning of Large Language Models
15. NeurIPS 2025FORLA: Federated Object-centric Representation Learning with Slot Attention
16. NeurIPS 2025Global Prompt Refinement with Non-Interfering Attention Masking for One-Shot Federated Learning
17. NeurIPS 2025Layer-wise Update Aggregation with Recycling for Communication-Efficient Federated Learning
18. NeurIPS 2025Learning Reconfigurable Representations for Multimodal Federated Learning with Missing Data
19. NeurIPS 2025MARS: A Malignity-Aware Backdoor Defense in Federated Learning
20. NeurIPS 2025Personalized Bayesian Federated Learning with Wasserstein Barycenter Aggregation
21. NeurIPS 2025Personalized Federated Conformal Prediction with Localization
22. NeurIPS 2025Personalized Subgraph Federated Learning with Differentiable Auxiliary Projections
23. NeurIPS 2025Resource-Constrained Federated Continual Learning: What Does Matter?
24. NeurIPS 2025Revisiting Consensus Error: A Fine-grained Analysis of Local SGD under Second-order Data Heterogeneity
25. NeurIPS 2025Rising from Ashes: Generalized Federated Learning via Dynamic Parameter Reset
26. NeurIPS 2025Sharp Gaussian approximations for Decentralized Federated Learning
27. NeurIPS 2025Soft-consensual Federated Learning for Data Heterogeneity via Multiple Paths
28. NeurIPS 2025Streaming Federated Learning with Markovian Data
29. NeurIPS 2025Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation
30. NeurIPS 2025Tracing Back the Malicious Clients in Poisoning Attacks to Federated Learning
31. NeurIPS 2025Vertical Federated Feature Screening

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。