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 43 篇，第 1 / 1 页

1. CVPR 2023How To Prevent the Poor Performance Clients for Personalized Federated Learning?
2. CVPR 2023Make Landscape Flatter in Differentially Private Federated Learning
3. CVPR 2023On the Effectiveness of Partial Variance Reduction in Federated Learning With Heterogeneous Data
4. CVPR 2023Re-Thinking Federated Active Learning Based on Inter-Class Diversity
5. CVPR 2023ScaleFL: Resource-Adaptive Federated Learning With Heterogeneous Clients
6. CVPR 2023The Resource Problem of Using Linear Layer Leakage Attack in Federated Learning
7. CVPR 2023The Treasure Beneath Multiple Annotations: An Uncertainty-Aware Edge Detector
8. ICLR 2023Combating Exacerbated Heterogeneity for Robust Models in Federated Learning
9. ICLR 2023DepthFL : Depthwise Federated Learning for Heterogeneous Clients
10. ICLR 2023Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach
11. ICLR 2023Federated Learning from Small Datasets
12. ICLR 2023Federated Nearest Neighbor Machine Translation
13. ICLR 2023Federated Neural Bandits
14. ICLR 2023FedExP: Speeding Up Federated Averaging via Extrapolation
15. ICLR 2023FedFA: Federated Feature Augmentation
16. ICLR 2023Instance-wise Batch Label Restoration via Gradients in Federated Learning
17. ICLR 2023Machine Unlearning of Federated Clusters
18. ICLR 2023PerFedMask: Personalized Federated Learning with Optimized Masking Vectors
19. ICLR 2023SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication
20. ICML 2023Achieving Linear Speedup in Non-IID Federated Bilevel Learning
21. ICML 2023Communication-Efficient Federated Hypergradient Computation via Aggregated Iterative Differentiation
22. ICML 2023Doubly Adversarial Federated Bandits
23. ICML 2023Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction
24. ICML 2023FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction
25. ICML 2023Federated Adversarial Learning: A Framework with Convergence Analysis
26. ICML 2023Federated Heavy Hitter Recovery under Linear Sketching
27. ICML 2023Improving the Model Consistency of Decentralized Federated Learning
28. ICML 2023On the Convergence of Federated Averaging with Cyclic Client Participation
29. ICML 2023Personalized Federated Learning under Mixture of Distributions
30. ICML 2023Private Federated Learning with Autotuned Compression
31. ICML 2023Second-order regression models exhibit progressive sharpening to the edge of stability
32. ICML 2023TabLeak: Tabular Data Leakage in Federated Learning
33. NeurIPS 2023Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained Devices
34. NeurIPS 2023Fed-FA: Theoretically Modeling Client Data Divergence for Federated Language Backdoor Defense
35. NeurIPS 2023Federated Compositional Deep AUC Maximization
36. NeurIPS 2023Federated Learning with Bilateral Curation for Partially Class-Disjoint Data
37. NeurIPS 2023Federated Linear Bandits with Finite Adversarial Actions
38. NeurIPS 2023FedFed: Feature Distillation against Data Heterogeneity in Federated Learning
39. NeurIPS 2023FLuID: Mitigating Stragglers in Federated Learning using Invariant Dropout
40. NeurIPS 2023From Trainable Negative Depth to Edge Heterophily in Graphs
41. NeurIPS 2023Private Federated Frequency Estimation: Adapting to the Hardness of the Instance
42. NeurIPS 2023TIES-Merging: Resolving Interference When Merging Models
43. NeurIPS 2023Towards Personalized Federated Learning via Heterogeneous Model Reassembly

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。