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 54 篇，第 1 / 2 页

1. NeurIPS 2026A typed tensor language for federated learning
2. NeurIPS 2026ASAP: Fast Adaptive Sliding Agnostic Poisoning Attack on Federated Learning
3. NeurIPS 2026Does Sparse Connectivity Improve Generalization? Convolutional Networks Below the Edge of Stability
4. NeurIPS 2026Edge of Stability Selectively Shapes Learning Across the Data Distribution
5. NeurIPS 2026Edge of Stochastic Stability: Revisiting the Edge of Stability for SGD
6. NeurIPS 2026FedSEM: Mitigating Cross-Client Evidence Drift in Federated Multiple Instance Learning
7. NeurIPS 2026FedSOUL: Federated Continual Unlearning via Spectral Orthogonality
8. NeurIPS 2026Generalization at the Edge of Stability
9. NeurIPS 2026Geometry-Constrained Kolmogorov–Arnold Networks: Learning Edge Geometry via Banach Duality
10. NeurIPS 2026Geometry-Regularized Collapse Resistance via Consensus Enhancement for Federated Learning
11. NeurIPS 2026MainFL: Intertemporal Data Valuation for Robust Auction-based Federated Learning
12. NeurIPS 2026Rehearsal-Free Statistical Prototype Regularization for Federated Incremental Learning
13. NeurIPS 2026SGD at the Edge of Stability: The Stochastic Sharpness Gap
14. NeurIPS 2026ShadowFPT: Backdooring Federated Prompt Tuning via Shadow Triggers
15. NeurIPS 2026Stability-Enhanced Federated Learning with Accelerated Gradient
16. NeurIPS 2025Class-wise Balancing Data Replay for Federated Class-Incremental Learning
17. NeurIPS 2025Covariances for Free: Exploiting Mean Distributions for Training-free Federated Learning
18. NeurIPS 2025FedEL: Federated Elastic Learning for Heterogeneous Devices
19. NeurIPS 2025FedFree: Breaking Knowledge-sharing Barriers through Layer-wise Alignment in Heterogeneous Federated Learning
20. NeurIPS 2025FedGPS: Statistical Rectification Against Data Heterogeneity in Federated Learning
21. NeurIPS 2025FedRTS: Federated Robust Pruning via Combinatorial Thompson Sampling
22. NeurIPS 2025Flick: Empowering Federated Learning with Commonsense Knowledge
23. NeurIPS 2025HYPERION: Fine-Grained Hypersphere Alignment for Robust Federated Graph Learning
24. NeurIPS 2025LLM at Network Edge: A Layer-wise Efficient Federated Fine-tuning Approach
25. NeurIPS 2025On the Edge of Memorization in Diffusion Models
26. NeurIPS 2025Personalized Subgraph Federated Learning with Differentiable Auxiliary Projections
27. NeurIPS 2025Ravan: Multi-Head Low-Rank Adaptation for Federated Fine-Tuning
28. NeurIPS 2025Resource-Constrained Federated Continual Learning: What Does Matter?
29. NeurIPS 2025Rising from Ashes: Generalized Federated Learning via Dynamic Parameter Reset
30. NeurIPS 2025Soft-consensual Federated Learning for Data Heterogeneity via Multiple Paths
31. NeurIPS 2025Streaming Federated Learning with Markovian Data
32. NeurIPS 2025Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation
33. NeurIPS 2025Towards Straggler-Resilient Split Federated Learning: An Unbalanced Update Approach
34. NeurIPS 2025Transfer Learning on Edge Connecting Probability Estimation Under Graphon Model
35. NeurIPS 2025Variational Learning Finds Flatter Solutions at the Edge of Stability
36. NeurIPS 2024A-FedPD: Aligning Dual-Drift is All Federated Primal-Dual Learning Needs
37. NeurIPS 2024Confusion-Resistant Federated Learning via Diffusion-Based Data Harmonization on Non-IID Data
38. NeurIPS 2024DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices
39. NeurIPS 2024FACT or Fiction: Can Truthful Mechanisms Eliminate Federated Free Riding?
40. NeurIPS 2024Federated Learning over Connected Modes
41. NeurIPS 2024Low Precision Local Training is Enough for Federated Learning
42. NeurIPS 2024The Sample-Communication Complexity Trade-off in Federated Q-Learning
43. NeurIPS 2024Thinking Forward: Memory-Efficient Federated Finetuning of Language Models
44. NeurIPS 2023Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained Devices
45. NeurIPS 2023Fed-FA: Theoretically Modeling Client Data Divergence for Federated Language Backdoor Defense
46. NeurIPS 2023Federated Compositional Deep AUC Maximization
47. NeurIPS 2023Federated Learning with Bilateral Curation for Partially Class-Disjoint Data
48. NeurIPS 2023Federated Linear Bandits with Finite Adversarial Actions
49. NeurIPS 2023FedFed: Feature Distillation against Data Heterogeneity in Federated Learning
50. NeurIPS 2023FLuID: Mitigating Stragglers in Federated Learning using Invariant Dropout

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。