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 29 篇，第 1 / 1 页

1. CVPR 2026E2-SCI Elastic Edge-Cloud Speculative Decoding via Credit Inertia
2. CVPR 2026Edge-Focused Super-Resolution for Omnidirectional Images with Spherical Geometric Augmentation
3. CVPR 2026From Selection to Scheduling Federated Geometry-Aware Correction Makes Exemplar Replay
4. CVPR 2026Single-Round Scalable Analytic Federated Learning
5. CVPR 2026Submodel Extraction for Efficient and Personalized Federated Learning via Optimal
6. CVPR 2025AFL: A Single-Round Analytic Approach for Federated Learning with Pre-trained Models
7. CVPR 2025Beyond Local Sharpness: Communication-Efficient Global Sharpness-aware Minimization for Federated Learning
8. CVPR 2025FedCS: Coreset Selection for Federated Learning
9. CVPR 2025NoT: Federated Unlearning via Weight Negation
10. CVPR 2025Population Normalization for Federated Learning
11. CVPR 20243D Neural Edge Reconstruction
12. CVPR 2024An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated Learning
13. CVPR 2024Communication-Efficient Federated Learning with Accelerated Client Gradient
14. CVPR 2024Device-Wise Federated Network Pruning
15. CVPR 2024DiPrompT: Disentangled Prompt Tuning for Multiple Latent Domain Generalization in Federated Learning
16. CVPR 2024FedAS: Bridging Inconsistency in Personalized Federated Learning
17. CVPR 2024FedSOL: Stabilized Orthogonal Learning with Proximal Restrictions in Federated Learning
18. CVPR 2024Mixed-Precision Quantization for Federated Learning on Resource-Constrained Heterogeneous Devices
19. CVPR 2024Relaxed Contrastive Learning for Federated Learning
20. CVPR 2024Revamping Federated Learning Security from a Defender's Perspective: A Unified Defense with Homomorphic Encrypted Data Space
21. CVPR 2024Towards Efficient Replay in Federated Incremental Learning
22. CVPR 2024Traceable Federated Continual Learning
23. CVPR 2023How To Prevent the Poor Performance Clients for Personalized Federated Learning?
24. CVPR 2023Make Landscape Flatter in Differentially Private Federated Learning
25. CVPR 2023On the Effectiveness of Partial Variance Reduction in Federated Learning With Heterogeneous Data
26. CVPR 2023Re-Thinking Federated Active Learning Based on Inter-Class Diversity
27. CVPR 2023ScaleFL: Resource-Adaptive Federated Learning With Heterogeneous Clients
28. CVPR 2023The Resource Problem of Using Linear Layer Leakage Attack in Federated Learning
29. CVPR 2023The Treasure Beneath Multiple Annotations: An Uncertainty-Aware Edge Detector

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。