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 89 篇，第 2 / 2 页

51. ICLR 2024Heterogeneous Personalized Federated Learning by Local-Global Updates Mixing via Convergence Rate
52. ICLR 2024Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learning
53. ICLR 2024Incentivized Truthful Communication for Federated Bandits
54. ICLR 2024Internal Cross-layer Gradients for Extending Homogeneity to Heterogeneity in Federated Learning
55. ICLR 2024Learning Personalized Causally Invariant Representations for Heterogeneous Federated Clients
56. ICLR 2024On Differentially Private Federated Linear Contextual Bandits
57. ICLR 2024PeFLL: Personalized Federated Learning by Learning to Learn
58. ICLR 2024Robust Training of Federated Models with Extremely Label Deficiency
59. ICLR 2024Stochastic Controlled Averaging for Federated Learning with Communication Compression
60. ICLR 2024Tackling the Data Heterogeneity in Asynchronous Federated Learning with Cached Update Calibration
61. ICLR 2023Better Generative Replay for Continual Federated Learning
62. ICLR 2023Bias Propagation in Federated Learning
63. ICLR 2023Combating Exacerbated Heterogeneity for Robust Models in Federated Learning
64. ICLR 2023Data-Free One-Shot Federated Learning Under Very High Statistical Heterogeneity
65. ICLR 2023Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models
66. ICLR 2023DepthFL : Depthwise Federated Learning for Heterogeneous Clients
67. ICLR 2023Effective passive membership inference attacks in federated learning against overparameterized models
68. ICLR 2023Efficient Federated Domain Translation
69. ICLR 2023EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous Data
70. ICLR 2023FedDAR: Federated Domain-Aware Representation Learning
71. ICLR 2023Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach
72. ICLR 2023Federated Learning from Small Datasets
73. ICLR 2023Federated Nearest Neighbor Machine Translation
74. ICLR 2023Federated Neural Bandits
75. ICLR 2023FedExP: Speeding Up Federated Averaging via Extrapolation
76. ICLR 2023FedFA: Federated Feature Augmentation
77. ICLR 2023FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning
78. ICLR 2023Instance-wise Batch Label Restoration via Gradients in Federated Learning
79. ICLR 2023Learning to CROSS exchange to solve min-max vehicle routing problems
80. ICLR 2023Machine Unlearning of Federated Clusters
81. ICLR 2023Meta Knowledge Condensation for Federated Learning
82. ICLR 2023MocoSFL: enabling cross-client collaborative self-supervised learning
83. ICLR 2023Multimodal Federated Learning via Contrastive Representation Ensemble
84. ICLR 2023PerFedMask: Personalized Federated Learning with Optimized Masking Vectors
85. ICLR 2023Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses
86. ICLR 2023SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication
87. ICLR 2023Test-Time Robust Personalization for Federated Learning
88. ICLR 2023Towards Addressing Label Skews in One-Shot Federated Learning
89. ICLR 2023Turning the Curse of Heterogeneity in Federated Learning into a Blessing for Out-of-Distribution Detection

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。