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 8 / 10 页

351. ICLR 2023Efficient Federated Domain Translation
352. ICLR 2023EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous Data
353. ICLR 2023FedDAR: Federated Domain-Aware Representation Learning
354. ICLR 2023Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach
355. ICLR 2023Federated Learning from Small Datasets
356. ICLR 2023Federated Nearest Neighbor Machine Translation
357. ICLR 2023Federated Neural Bandits
358. ICLR 2023FedExP: Speeding Up Federated Averaging via Extrapolation
359. ICLR 2023FedFA: Federated Feature Augmentation
360. ICLR 2023FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning
361. ICLR 2023Instance-wise Batch Label Restoration via Gradients in Federated Learning
362. ICLR 2023Learning to CROSS exchange to solve min-max vehicle routing problems
363. ICLR 2023Machine Unlearning of Federated Clusters
364. ICLR 2023Meta Knowledge Condensation for Federated Learning
365. ICLR 2023MocoSFL: enabling cross-client collaborative self-supervised learning
366. ICLR 2023Multimodal Federated Learning via Contrastive Representation Ensemble
367. ICLR 2023PerFedMask: Personalized Federated Learning with Optimized Masking Vectors
368. ICLR 2023Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses
369. ICLR 2023SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication
370. ICLR 2023Test-Time Robust Personalization for Federated Learning
371. ICLR 2023Towards Addressing Label Skews in One-Shot Federated Learning
372. ICLR 2023Turning the Curse of Heterogeneity in Federated Learning into a Blessing for Out-of-Distribution Detection
373. ICML 2023Achieving Linear Speedup in Non-IID Federated Bilevel Learning
374. ICML 2023Anchor Sampling for Federated Learning with Partial Client Participation
375. ICML 2023Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learning
376. ICML 2023Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning Using Independent Component Analysis
377. ICML 2023Communication-Efficient Federated Hypergradient Computation via Aggregated Iterative Differentiation
378. ICML 2023Conformal Prediction for Federated Uncertainty Quantification Under Label Shift
379. ICML 2023DoCoFL: Downlink Compression for Cross-Device Federated Learning
380. ICML 2023Doubly Adversarial Federated Bandits
381. ICML 2023Fast Federated Machine Unlearning with Nonlinear Functional Theory
382. ICML 2023FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction
383. ICML 2023FedCR: Personalized Federated Learning Based on Across-Client Common Representation with Conditional Mutual Information Regularization
384. ICML 2023FedDisco: Federated Learning with Discrepancy-Aware Collaboration
385. ICML 2023Federated Adversarial Learning: A Framework with Convergence Analysis
386. ICML 2023Federated Conformal Predictors for Distributed Uncertainty Quantification
387. ICML 2023Federated Heavy Hitter Recovery under Linear Sketching
388. ICML 2023FedHPO-Bench: A Benchmark Suite for Federated Hyperparameter Optimization
389. ICML 2023GuardHFL: Privacy Guardian for Heterogeneous Federated Learning
390. ICML 2023Improving the Model Consistency of Decentralized Federated Learning
391. ICML 2023LeadFL: Client Self-Defense against Model Poisoning in Federated Learning
392. ICML 2023LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning
393. ICML 2023No One Idles: Efficient Heterogeneous Federated Learning with Parallel Edge and Server Computation
394. ICML 2023On the Convergence of Federated Averaging with Cyclic Client Participation
395. ICML 2023One-Shot Federated Conformal Prediction
396. ICML 2023Optimizing the Collaboration Structure in Cross-Silo Federated Learning
397. ICML 2023Out-of-Distribution Generalization of Federated Learning via Implicit Invariant Relationships
398. ICML 2023Personalized Federated Learning under Mixture of Distributions
399. ICML 2023Personalized Federated Learning with Inferred Collaboration Graphs
400. ICML 2023Personalized Subgraph Federated Learning

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。