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 60 篇，第 1 / 2 页

1. ICML 2026Benchmarking at the Edge of Comprehension
2. ICML 2026Breaking the Capacity Bottleneck in Model-Heterogeneous Federated Learning via Gradual Model Restoration
3. ICML 2026Conflicting Biases at the Edge of Stability: Norm versus Sharpness Regularization
4. ICML 2026Credibility-Aware Weighting Federated Causal Discovery for Time Series
5. ICML 2026Edge-colored Clustering in Hypergraphs: A MaxECC Approximation
6. ICML 2026FedEMoE: Improving Personalization on Heterogeneous Federated Learning via Elastic Mixture of Experts Architecture
7. ICML 2026FedFit: Federated Dynamic Sparse Training via Fisher Information scoring
8. ICML 2026FedGain: Toward Negative-Gain-Free Client Collaboration in Federated Learning
9. ICML 2026FedReLa: Imbalanced Federated Learning via Re-Labeling
10. ICML 2026Influence-Disentangled Federated Training: Learning Models That Are Easy to Unlearn
11. ICML 2026Lightweight Federated Incremental Learning via Decoupled Replay
12. ICML 2026Momentum Further Constrains Sharpness at the Edge of Stochastic Stability
13. ICML 2026No Retraining at Edge: Efficient Resource-Aware Mixed-Precision Quantization via Federated Supernet Learning
14. ICML 2026Non-Euclidean Gradient Descent Operates at the Edge of Stability
15. ICML 2026PipeSD: An Efficient Cloud-Edge Collaborative Pipeline Inference Framework with Speculative Decoding
16. ICML 2026RADE: Random Add-Drop Edge as a Regularizer
17. ICML 2026ShapCCS: Shapley-Driven Client Coreset Selection in Federated Learning
18. ICML 2026Trustworthy Federated Label Distribution Learning under Annotation Quality Disparity
19. ICML 2025Constant Stepsize Local GD for Logistic Regression: Acceleration by Instability
20. ICML 2025Edge-Colored Clustering in Hypergraphs: Beyond Minimizing Unsatisfied Edges
21. ICML 2025FedECADO: A Dynamical System Model of Federated Learning
22. ICML 2025Federated Node-Level Clustering Network with Cross-Subgraph Link Mending
23. ICML 2025FedSSI: Rehearsal-Free Continual Federated Learning with Synergistic Synaptic Intelligence
24. ICML 2025Grokking at the Edge of Linear Separability
25. ICML 2025Hybrid Batch Normalisation: Resolving the Dilemma of Batch Normalisation in Federated Learning
26. ICML 2025Less is More: Federated Graph Learning with Alleviating Topology Heterogeneity from A Causal Perspective
27. ICML 2025NTK-DFL: Enhancing Decentralized Federated Learning in Heterogeneous Settings via Neural Tangent Kernel
28. ICML 2025Widening the Network Mitigates the Impact of Data Heterogeneity on FedAvg
29. ICML 2024Accelerating Federated Learning with Quick Distributed Mean Estimation
30. ICML 2024Accelerating Heterogeneous Federated Learning with Closed-form Classifiers
31. ICML 2024Achieving Lossless Gradient Sparsification via Mapping to Alternative Space in Federated Learning
32. ICML 2024Bridging Model Heterogeneity in Federated Learning via Uncertainty-based Asymmetrical Reciprocity Learning
33. ICML 2024Causally Motivated Personalized Federated Invariant Learning with Shortcut-Averse Information-Theoretic Regularization
34. ICML 2024Clustered Federated Learning via Gradient-based Partitioning
35. ICML 2024Decomposable Submodular Maximization in Federated Setting
36. ICML 2024Fair Federated Learning via the Proportional Veto Core
37. ICML 2024FedBAT: Communication-Efficient Federated Learning via Learnable Binarization
38. ICML 2024Federated Full-Parameter Tuning of Billion-Sized Language Models with Communication Cost under 18 Kilobytes
39. ICML 2024Federated Neuro-Symbolic Learning
40. ICML 2024Federated Representation Learning in the Under-Parameterized Regime
41. ICML 2024Federated Self-Explaining GNNs with Anti-shortcut Augmentations
42. ICML 2024FedMBridge: Bridgeable Multimodal Federated Learning
43. ICML 2024FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clustering
44. ICML 2024Improved Modelling of Federated Datasets using Mixtures-of-Dirichlet-Multinomials
45. ICML 2024Lessons from Generalization Error Analysis of Federated Learning: You May Communicate Less Often!
46. ICML 2024Recovering Labels from Local Updates in Federated Learning
47. ICML 2024Rethinking the Flat Minima Searching in Federated Learning
48. ICML 2023Achieving Linear Speedup in Non-IID Federated Bilevel Learning
49. ICML 2023Communication-Efficient Federated Hypergradient Computation via Aggregated Iterative Differentiation
50. ICML 2023Doubly Adversarial Federated Bandits

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。