联邦边缘推理论文趋势 2023–2026

主要结论

• 联邦边缘推理论文从 2023 年的 43 篇增长到 2026 年的 45 篇，三年间扩大到约 1.0 倍。
• ICML 是发联邦边缘推理论文最多的会议，2023–2026 年共 60 篇，占四个会议总量的 33%。
• 交叉方向里涨得最快的是端侧推理：同时属于两个主题的论文从 2023 年的 3 篇增至 2026 年的 15 篇。
• 在「边缘与物联网」的 5 个主题里，它的 2026 年论文数排第 2；同组增长最快的传感融合是 3.8 倍，联邦边缘推理是 1.0 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 2026Does Sparse Connectivity Improve Generalization? Convolutional Networks Below the Edge of Stability
• ICML 2026Breaking the Capacity Bottleneck in Model-Heterogeneous Federated Learning via Gradual Model Restoration
• ICLR 2026Beyond Aggregation: Guiding Clients in Heterogeneous Federated Learning
• CVPR 2026E2-SCI Elastic Edge-Cloud Speculative Decoding via Credit Inertia
• NeurIPS 2025Class-wise Balancing Data Replay for Federated Class-Incremental Learning
• ICML 2025Constant Stepsize Local GD for Logistic Regression: Acceleration by Instability
• ICLR 2025Connecting Federated ADMM to Bayes
• CVPR 2025Beyond Local Sharpness: Communication-Efficient Global Sharpness-aware Minimization for Federated Learning
• NeurIPS 2024DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices
• ICML 2024Achieving Lossless Gradient Sparsification via Mapping to Alternative Space in Federated Learning
• ICLR 2024FedTrans: Client-Transparent Utility Estimation for Robust Federated Learning
• CVPR 2024FedAS: Bridging Inconsistency in Personalized Federated Learning
• NeurIPS 2023Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained Devices
• ICML 2023Achieving Linear Speedup in Non-IID Federated Bilevel Learning
• ICLR 2023Combating Exacerbated Heterogeneity for Robust Models in Federated Learning
• CVPR 2023How To Prevent the Poor Performance Clients for Personalized Federated Learning?
• NeurIPS 2026Edge of Stability Selectively Shapes Learning Across the Data Distribution
• ICML 2026Influence-Disentangled Federated Training: Learning Models That Are Easy to Unlearn
• ICLR 2026DeepAFL: Deep Analytic Federated Learning
• CVPR 2026Edge-Focused Super-Resolution for Omnidirectional Images with Spherical Geometric Augmentation

全部联邦边缘推理论文

共 37 篇，第 1 / 1 页

1. ICLR 2026Beyond Aggregation: Guiding Clients in Heterogeneous Federated Learning
2. ICLR 2026DeepAFL: Deep Analytic Federated Learning
3. ICLR 2026Deploying Models to Non-participating Clients in Federated Learning without Fine-tuning: A Hypernetwork-based Approach
4. ICLR 2026Fed-Duet: Dual Expert-Orchestrated Framework for Continual Federated Vision-Language Learning
5. ICLR 2026Layerwise Federated Learning for Heterogeneous Quantum Clients using Quorus
6. ICLR 2026Near Optimal Robust Federated Learning Against Data Poisoning Attack
7. ICLR 2026Robust Federated Inference
8. ICLR 2025Can Textual Gradient Work in Federated Learning?
9. ICLR 2025Connecting Federated ADMM to Bayes
10. ICLR 2025Debiasing Federated Learning with Correlated Client Participation
11. ICLR 2025Decoupled Subgraph Federated Learning
12. ICLR 2025Enhancing Clustered Federated Learning: Integration of Strategies and Improved Methodologies
13. ICLR 2025FedLWS: Federated Learning with Adaptive Layer-wise Weight Shrinking
14. ICLR 2025Grokking at the Edge of Numerical Stability
15. ICLR 2025Learning Dynamics of Deep Matrix Factorization Beyond the Edge of Stability
16. ICLR 2025PRISM: Privacy-Preserving Improved Stochastic Masking for Federated Generative Models
17. ICLR 2025Unlocking the Potential of Model Calibration in Federated Learning
18. ICLR 2024Backdoor Federated Learning by Poisoning Backdoor-Critical Layers
19. ICLR 2024Federated Orthogonal Training: Mitigating Global Catastrophic Forgetting in Continual Federated Learning
20. ICLR 2024Federated Wasserstein Distance
21. ICLR 2024FedTrans: Client-Transparent Utility Estimation for Robust Federated Learning
22. ICLR 2024Heterogeneous Personalized Federated Learning by Local-Global Updates Mixing via Convergence Rate
23. ICLR 2024PeFLL: Personalized Federated Learning by Learning to Learn
24. ICLR 2024Robust Training of Federated Models with Extremely Label Deficiency
25. ICLR 2024VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarks
26. ICLR 2023Combating Exacerbated Heterogeneity for Robust Models in Federated Learning
27. ICLR 2023DepthFL : Depthwise Federated Learning for Heterogeneous Clients
28. ICLR 2023Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach
29. ICLR 2023Federated Learning from Small Datasets
30. ICLR 2023Federated Nearest Neighbor Machine Translation
31. ICLR 2023Federated Neural Bandits
32. ICLR 2023FedExP: Speeding Up Federated Averaging via Extrapolation
33. ICLR 2023FedFA: Federated Feature Augmentation
34. ICLR 2023Instance-wise Batch Label Restoration via Gradients in Federated Learning
35. ICLR 2023Machine Unlearning of Federated Clusters
36. ICLR 2023PerFedMask: Personalized Federated Learning with Optimized Masking Vectors
37. ICLR 2023SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication

常见问题

2023–2026 年四大会议一共有多少篇联邦边缘推理论文？

共 180 篇，按年份分别是 2023 年 43 篇、2024 年 47 篇、2025 年 45 篇、2026 年 45 篇。

NeurIPS 2026 有多少篇联邦边缘推理论文？

NeurIPS 2026 有 15 篇联邦边缘推理论文，上一届是 20 篇。

ICLR 2026 有多少篇联邦边缘推理论文？

ICLR 2026 有 7 篇联邦边缘推理论文，上一届是 10 篇。

ICML 2026 有多少篇联邦边缘推理论文？

ICML 2026 有 18 篇联邦边缘推理论文，上一届是 10 篇。

CVPR 2026 有多少篇联邦边缘推理论文？

CVPR 2026 有 5 篇联邦边缘推理论文，上一届是 5 篇。

哪个会议的联邦边缘推理论文最多？

ICML，2023–2026 年共 60 篇。