联邦学习论文趋势 2023–2026

主要结论

• 联邦学习论文从 2023 年的 137 篇降到 2026 年的 119 篇，热度在回落。
• NeurIPS 是发联邦学习论文最多的会议，2023–2026 年共 157 篇，占四个会议总量的 34%。
• 交叉方向里涨得最快的是低秩适配：同时属于两个主题的论文从 2023 年的 0 篇增至 2026 年的 12 篇。
• 在「联邦与隐私」的 5 个主题里，它的 2026 年论文数排第 2；同组增长最快的安全聚合是 1.8 倍，联邦学习是 0.9 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 2026A typed tensor language for federated learning
• ICML 2026BESplit: Bias-Compensated Split Federated Learning with Evidential Aggregation
• ICLR 2026Beyond Aggregation: Guiding Clients in Heterogeneous Federated Learning
• CVPR 2026FedRG Unleashing the Representation Geometry for Federated Learning with Noisy
• NeurIPS 2025Class-wise Balancing Data Replay for Federated Class-Incremental Learning
• ICML 2025Causality Inspired Federated Learning for OOD Generalization
• ICLR 2025Adaptive Gradient Clipping for Robust Federated Learning
• CVPR 2025FedCS: Coreset Selection for Federated Learning
• NeurIPS 2024Local Superior Soups: A Catalyst for Model Merging in Cross-Silo Federated Learning
• ICML 2024Achieving Lossless Gradient Sparsification via Mapping to Alternative Space in Federated Learning
• ICLR 2024FedTrans: Client-Transparent Utility Estimation for Robust Federated Learning
• CVPR 2024Decentralized Directed Collaboration for Personalized Federated Learning
• NeurIPS 2023DELTA: Diverse Client Sampling for Fasting Federated Learning
• ICML 2023Achieving Linear Speedup in Non-IID Federated Bilevel Learning
• ICLR 2023Combating Exacerbated Heterogeneity for Robust Models in Federated Learning
• CVPR 2023Federated Domain Generalization With Generalization Adjustment
• NeurIPS 2026Consistency-Verified Backdoor Defense for Federated Graph Learning via Cross-Layer Drift
• ICML 2026Breaking the Capacity Bottleneck in Model-Heterogeneous Federated Learning via Gradual Model Restoration
• ICLR 2026DeepAFL: Deep Analytic Federated Learning
• CVPR 2026FedSST Rethinking Fair Federated Graph Learning under Structural Shift

全部联邦学习论文

共 460 篇，第 4 / 10 页

151. ICLR 2025On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations
152. ICLR 2025Problem-Parameter-Free Federated Learning
153. ICLR 2025SparsyFed: Sparse Adaptive Federated Learning
154. ICLR 2025Subgraph Federated Learning for Local Generalization
155. ICLR 2025Towards Federated RLHF with Aggregated Client Preference for LLMs
156. ICLR 2025Unlocking the Potential of Model Calibration in Federated Learning
157. ICML 2025Causality Inspired Federated Learning for OOD Generalization
158. ICML 2025FedBEns: One-Shot Federated Learning based on Bayesian Ensemble
159. ICML 2025FedECADO: A Dynamical System Model of Federated Learning
160. ICML 2025Federated Generalised Variational Inference: A Robust Probabilistic Federated Learning Framework
161. ICML 2025Federated In-Context Learning: Iterative Refinement for Improved Answer Quality
162. ICML 2025Federated Learning for Feature Generalization with Convex Constraints
163. ICML 2025Federated Node-Level Clustering Network with Cross-Subgraph Link Mending
164. ICML 2025FedSSI: Rehearsal-Free Continual Federated Learning with Synergistic Synaptic Intelligence
165. ICML 2025Ferret: Federated Full-Parameter Tuning at Scale for Large Language Models
166. ICML 2025FSL-SAGE: Accelerating Federated Split Learning via Smashed Activation Gradient Estimation
167. ICML 2025Gap-Dependent Bounds for Federated $Q$-Learning
168. ICML 2025Harnessing Heterogeneous Statistical Strength for Personalized Federated Learning via Hierarchical Bayesian Inference
169. ICML 2025Hybrid Batch Normalisation: Resolving the Dilemma of Batch Normalisation in Federated Learning
170. ICML 2025Improving Generalization in Federated Learning with Highly Heterogeneous Data via Momentum-Based Stochastic Controlled Weight Averaging
171. ICML 2025LBI-FL: Low-Bit Integerized Federated Learning with Temporally Dynamic Bit-Width Allocation
172. ICML 2025Local Pan-privacy for Federated Analytics
173. ICML 2025Momentum-Driven Adaptivity: Towards Tuning-Free Asynchronous Federated Learning
174. ICML 2025NTK-DFL: Enhancing Decentralized Federated Learning in Heterogeneous Settings via Neural Tangent Kernel
175. ICML 2025One Arrow, Two Hawks: Sharpness-aware Minimization for Federated Learning via Global Model Trajectory
176. ICML 2025Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead
177. ICML 2025SecEmb: Sparsity-Aware Secure Federated Learning of On-Device Recommender System with Large Embedding
178. ICML 2025Splitting with Importance-aware Updating for Heterogeneous Federated Learning with Large Language Models
179. ICML 2025Widening the Network Mitigates the Impact of Data Heterogeneity on FedAvg
180. NeurIPS 2025A Fair Federated Learning Method for Handling Client Participation Probability Inconsistencies in Heterogeneous Environments
181. NeurIPS 2025A Reinforcement Learning-based Bidding Strategy for Data Consumers in Auction-based Federated Learning
182. NeurIPS 2025Accelerated Vertical Federated Adversarial Learning through Decoupling Layer-Wise Dependencies
183. NeurIPS 2025Adaptive Latent-Space Constraints in Personalized Federated Learning
184. NeurIPS 2025Class-wise Balancing Data Replay for Federated Class-Incremental Learning
185. NeurIPS 2025Competitive Advantage Attacks to Decentralized Federated Learning
186. NeurIPS 2025Covariances for Free: Exploiting Mean Distributions for Training-free Federated Learning
187. NeurIPS 2025Decentralized Dynamic Cooperation of Personalized Models for Federated Continual Learning
188. NeurIPS 2025Feature Distillation is the Better Choice for Model-Heterogeneous Federated Learning
189. NeurIPS 2025FedEL: Federated Elastic Learning for Heterogeneous Devices
190. NeurIPS 2025FedGPS: Statistical Rectification Against Data Heterogeneity in Federated Learning
191. NeurIPS 2025FedIGL: Federated Invariant Graph Learning for Non-IID Graphs
192. NeurIPS 2025FedRTS: Federated Robust Pruning via Combinatorial Thompson Sampling
193. NeurIPS 2025FlowerTune: A Cross-Domain Benchmark for Federated Fine-Tuning of Large Language Models
194. NeurIPS 2025FORLA: Federated Object-centric Representation Learning with Slot Attention
195. NeurIPS 2025Global Prompt Refinement with Non-Interfering Attention Masking for One-Shot Federated Learning
196. NeurIPS 2025Layer-wise Update Aggregation with Recycling for Communication-Efficient Federated Learning
197. NeurIPS 2025Learning Reconfigurable Representations for Multimodal Federated Learning with Missing Data
198. NeurIPS 2025MARS: A Malignity-Aware Backdoor Defense in Federated Learning
199. NeurIPS 2025Personalized Bayesian Federated Learning with Wasserstein Barycenter Aggregation
200. NeurIPS 2025Personalized Federated Conformal Prediction with Localization

常见问题

2023–2026 年四大会议一共有多少篇联邦学习论文？

共 460 篇，按年份分别是 2023 年 137 篇、2024 年 113 篇、2025 年 91 篇、2026 年 119 篇。

NeurIPS 2026 有多少篇联邦学习论文？

NeurIPS 2026 有 42 篇联邦学习论文，上一届是 31 篇。

ICLR 2026 有多少篇联邦学习论文？

ICLR 2026 有 19 篇联邦学习论文，上一届是 18 篇。

ICML 2026 有多少篇联邦学习论文？

ICML 2026 有 37 篇联邦学习论文，上一届是 23 篇。

CVPR 2026 有多少篇联邦学习论文？

CVPR 2026 有 21 篇联邦学习论文，上一届是 19 篇。

哪个会议的联邦学习论文最多？

NeurIPS，2023–2026 年共 157 篇。