联邦边缘推理论文趋势 2023–2026
边缘联邦
统计范围：NeurIPS、ICLR、ICML、CVPR 四个会议，2023–2026 年，共 180 篇被归入该主题的论文。
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。
各会议论文数
按年份和会议统计
| 年份 | NeurIPS | ICLR | ICML | CVPR | 合计 |
|---|---|---|---|---|---|
| 2023 | 11 | 12 | 13 | 7 | 43 |
| 2024 | 8 | 8 | 19 | 12 | 47 |
| 2025 | 20 | 10 | 10 | 5 | 45 |
| 2026 | 15 | 7 | 18 | 5 | 45 |
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 8 篇，第 1 / 1 页
- NeurIPS 2024A-FedPD: Aligning Dual-Drift is All Federated Primal-Dual Learning Needs
- NeurIPS 2024Confusion-Resistant Federated Learning via Diffusion-Based Data Harmonization on Non-IID Data
- NeurIPS 2024DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices
- NeurIPS 2024FACT or Fiction: Can Truthful Mechanisms Eliminate Federated Free Riding?
- NeurIPS 2024Federated Learning over Connected Modes
- NeurIPS 2024Low Precision Local Training is Enough for Federated Learning
- NeurIPS 2024The Sample-Communication Complexity Trade-off in Federated Q-Learning
- NeurIPS 2024Thinking Forward: Memory-Efficient Federated Finetuning of Language Models
在 Claude、Cursor 里直接问这个趋势
接上 Paperlist MCP 后，写综述、挑方向或准备投稿时，不用离开对话框就能拿到这页的数字：每年各会议的论文数、增长倍数、涨得最快的子方向和代表论文，AI 会按你的问题整理。
可以直接复制这句话问 AI
用 paperlist 查联邦边缘推理在 ICML 2023–2026 的论文趋势，哪一年涨得最快？
AI 会调用的工具：get_topic_stats {"topic": "federated-edge-inference"}
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。