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 12 篇，第 1 / 1 页

1. CVPR 20243D Neural Edge Reconstruction
2. CVPR 2024An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated Learning
3. CVPR 2024Communication-Efficient Federated Learning with Accelerated Client Gradient
4. CVPR 2024Device-Wise Federated Network Pruning
5. CVPR 2024DiPrompT: Disentangled Prompt Tuning for Multiple Latent Domain Generalization in Federated Learning
6. CVPR 2024FedAS: Bridging Inconsistency in Personalized Federated Learning
7. CVPR 2024FedSOL: Stabilized Orthogonal Learning with Proximal Restrictions in Federated Learning
8. CVPR 2024Mixed-Precision Quantization for Federated Learning on Resource-Constrained Heterogeneous Devices
9. CVPR 2024Relaxed Contrastive Learning for Federated Learning
10. CVPR 2024Revamping Federated Learning Security from a Defender's Perspective: A Unified Defense with Homomorphic Encrypted Data Space
11. CVPR 2024Towards Efficient Replay in Federated Incremental Learning
12. CVPR 2024Traceable Federated Continual Learning

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。