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 7 / 10 页

301. NeurIPS 2024FedAvP: Augment Local Data via Shared Policy in Federated Learning
302. NeurIPS 2024Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning
303. NeurIPS 2024Federated Ensemble-Directed Offline Reinforcement Learning
304. NeurIPS 2024Federated Graph Learning for Cross-Domain Recommendation
305. NeurIPS 2024Federated Learning over Connected Modes
306. NeurIPS 2024Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and Analysis
307. NeurIPS 2024Federated Model Heterogeneous Matryoshka Representation Learning
308. NeurIPS 2024FedGTST: Boosting Global Transferability of Federated Models via Statistics Tuning
309. NeurIPS 2024FedLPA: One-shot Federated Learning with Layer-Wise Posterior Aggregation
310. NeurIPS 2024Ferrari: Federated Feature Unlearning via Optimizing Feature Sensitivity
311. NeurIPS 2024FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Model Fusion
312. NeurIPS 2024Hierarchical Federated Learning with Multi-Timescale Gradient Correction
313. NeurIPS 2024Local Superior Soups: A Catalyst for Model Merging in Cross-Silo Federated Learning
314. NeurIPS 2024Nonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Data
315. NeurIPS 2024Parameter Disparities Dissection for Backdoor Defense in Heterogeneous Federated Learning
316. NeurIPS 2024pFedClub: Controllable Heterogeneous Model Aggregation for Personalized Federated Learning
317. NeurIPS 2024Revisiting Ensembling in One-Shot Federated Learning
318. NeurIPS 2024SPEAR: Exact Gradient Inversion of Batches in Federated Learning
319. NeurIPS 2024Taming Cross-Domain Representation Variance in Federated Prototype Learning with Heterogeneous Data Domains
320. NeurIPS 2024The Power of Extrapolation in Federated Learning
321. NeurIPS 2024The Sample-Communication Complexity Trade-off in Federated Q-Learning
322. NeurIPS 2024Towards Diverse Device Heterogeneous Federated Learning via Task Arithmetic Knowledge Integration
323. NeurIPS 2024Why Go Full? Elevating Federated Learning Through Partial Network Updates
324. CVPR 2023Adaptive Channel Sparsity for Federated Learning Under System Heterogeneity
325. CVPR 2023Bias-Eliminating Augmentation Learning for Debiased Federated Learning
326. CVPR 2023Confidence-Aware Personalized Federated Learning via Variational Expectation Maximization
327. CVPR 2023DaFKD: Domain-Aware Federated Knowledge Distillation
328. CVPR 2023Efficient On-Device Training via Gradient Filtering
329. CVPR 2023FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning
330. CVPR 2023Federated Domain Generalization With Generalization Adjustment
331. CVPR 2023Federated Incremental Semantic Segmentation
332. CVPR 2023Federated Learning With Data-Agnostic Distribution Fusion
333. CVPR 2023FedSeg: Class-Heterogeneous Federated Learning for Semantic Segmentation
334. CVPR 2023GradMA: A Gradient-Memory-Based Accelerated Federated Learning With Alleviated Catastrophic Forgetting
335. CVPR 2023How To Prevent the Poor Performance Clients for Personalized Federated Learning?
336. CVPR 2023Learning Federated Visual Prompt in Null Space for MRI Reconstruction
337. CVPR 2023Make Landscape Flatter in Differentially Private Federated Learning
338. CVPR 2023On the Effectiveness of Partial Variance Reduction in Federated Learning With Heterogeneous Data
339. CVPR 2023Re-Thinking Federated Active Learning Based on Inter-Class Diversity
340. CVPR 2023Rethinking Federated Learning With Domain Shift: A Prototype View
341. CVPR 2023ScaleFL: Resource-Adaptive Federated Learning With Heterogeneous Clients
342. CVPR 2023STDLens: Model Hijacking-Resilient Federated Learning for Object Detection
343. CVPR 2023The Resource Problem of Using Linear Layer Leakage Attack in Federated Learning
344. ICLR 2023Better Generative Replay for Continual Federated Learning
345. ICLR 2023Bias Propagation in Federated Learning
346. ICLR 2023Combating Exacerbated Heterogeneity for Robust Models in Federated Learning
347. ICLR 2023Data-Free One-Shot Federated Learning Under Very High Statistical Heterogeneity
348. ICLR 2023Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models
349. ICLR 2023DepthFL : Depthwise Federated Learning for Heterogeneous Clients
350. ICLR 2023Effective passive membership inference attacks in federated learning against overparameterized models

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。