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 180 篇，第 3 / 4 页

101. CVPR 2024Towards Efficient Replay in Federated Incremental Learning
102. CVPR 2024Traceable Federated Continual Learning
103. ICLR 2024Backdoor Federated Learning by Poisoning Backdoor-Critical Layers
104. ICLR 2024Federated Orthogonal Training: Mitigating Global Catastrophic Forgetting in Continual Federated Learning
105. ICLR 2024Federated Wasserstein Distance
106. ICLR 2024FedTrans: Client-Transparent Utility Estimation for Robust Federated Learning
107. ICLR 2024Heterogeneous Personalized Federated Learning by Local-Global Updates Mixing via Convergence Rate
108. ICLR 2024PeFLL: Personalized Federated Learning by Learning to Learn
109. ICLR 2024Robust Training of Federated Models with Extremely Label Deficiency
110. ICLR 2024VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarks
111. ICML 2024Accelerating Federated Learning with Quick Distributed Mean Estimation
112. ICML 2024Accelerating Heterogeneous Federated Learning with Closed-form Classifiers
113. ICML 2024Achieving Lossless Gradient Sparsification via Mapping to Alternative Space in Federated Learning
114. ICML 2024Bridging Model Heterogeneity in Federated Learning via Uncertainty-based Asymmetrical Reciprocity Learning
115. ICML 2024Causally Motivated Personalized Federated Invariant Learning with Shortcut-Averse Information-Theoretic Regularization
116. ICML 2024Clustered Federated Learning via Gradient-based Partitioning
117. ICML 2024Decomposable Submodular Maximization in Federated Setting
118. ICML 2024Fair Federated Learning via the Proportional Veto Core
119. ICML 2024FedBAT: Communication-Efficient Federated Learning via Learnable Binarization
120. ICML 2024Federated Full-Parameter Tuning of Billion-Sized Language Models with Communication Cost under 18 Kilobytes
121. ICML 2024Federated Neuro-Symbolic Learning
122. ICML 2024Federated Representation Learning in the Under-Parameterized Regime
123. ICML 2024Federated Self-Explaining GNNs with Anti-shortcut Augmentations
124. ICML 2024FedMBridge: Bridgeable Multimodal Federated Learning
125. ICML 2024FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clustering
126. ICML 2024Improved Modelling of Federated Datasets using Mixtures-of-Dirichlet-Multinomials
127. ICML 2024Lessons from Generalization Error Analysis of Federated Learning: You May Communicate Less Often!
128. ICML 2024Recovering Labels from Local Updates in Federated Learning
129. ICML 2024Rethinking the Flat Minima Searching in Federated Learning
130. NeurIPS 2024A-FedPD: Aligning Dual-Drift is All Federated Primal-Dual Learning Needs
131. NeurIPS 2024Confusion-Resistant Federated Learning via Diffusion-Based Data Harmonization on Non-IID Data
132. NeurIPS 2024DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices
133. NeurIPS 2024FACT or Fiction: Can Truthful Mechanisms Eliminate Federated Free Riding?
134. NeurIPS 2024Federated Learning over Connected Modes
135. NeurIPS 2024Low Precision Local Training is Enough for Federated Learning
136. NeurIPS 2024The Sample-Communication Complexity Trade-off in Federated Q-Learning
137. NeurIPS 2024Thinking Forward: Memory-Efficient Federated Finetuning of Language Models
138. CVPR 2023How To Prevent the Poor Performance Clients for Personalized Federated Learning?
139. CVPR 2023Make Landscape Flatter in Differentially Private Federated Learning
140. CVPR 2023On the Effectiveness of Partial Variance Reduction in Federated Learning With Heterogeneous Data
141. CVPR 2023Re-Thinking Federated Active Learning Based on Inter-Class Diversity
142. CVPR 2023ScaleFL: Resource-Adaptive Federated Learning With Heterogeneous Clients
143. CVPR 2023The Resource Problem of Using Linear Layer Leakage Attack in Federated Learning
144. CVPR 2023The Treasure Beneath Multiple Annotations: An Uncertainty-Aware Edge Detector
145. ICLR 2023Combating Exacerbated Heterogeneity for Robust Models in Federated Learning
146. ICLR 2023DepthFL : Depthwise Federated Learning for Heterogeneous Clients
147. ICLR 2023Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach
148. ICLR 2023Federated Learning from Small Datasets
149. ICLR 2023Federated Nearest Neighbor Machine Translation
150. ICLR 2023Federated Neural 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。