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 157 篇，第 2 / 4 页

51. NeurIPS 2025Feature Distillation is the Better Choice for Model-Heterogeneous Federated Learning
52. NeurIPS 2025FedEL: Federated Elastic Learning for Heterogeneous Devices
53. NeurIPS 2025FedGPS: Statistical Rectification Against Data Heterogeneity in Federated Learning
54. NeurIPS 2025FedIGL: Federated Invariant Graph Learning for Non-IID Graphs
55. NeurIPS 2025FedRTS: Federated Robust Pruning via Combinatorial Thompson Sampling
56. NeurIPS 2025FlowerTune: A Cross-Domain Benchmark for Federated Fine-Tuning of Large Language Models
57. NeurIPS 2025FORLA: Federated Object-centric Representation Learning with Slot Attention
58. NeurIPS 2025Global Prompt Refinement with Non-Interfering Attention Masking for One-Shot Federated Learning
59. NeurIPS 2025Layer-wise Update Aggregation with Recycling for Communication-Efficient Federated Learning
60. NeurIPS 2025Learning Reconfigurable Representations for Multimodal Federated Learning with Missing Data
61. NeurIPS 2025MARS: A Malignity-Aware Backdoor Defense in Federated Learning
62. NeurIPS 2025Personalized Bayesian Federated Learning with Wasserstein Barycenter Aggregation
63. NeurIPS 2025Personalized Federated Conformal Prediction with Localization
64. NeurIPS 2025Personalized Subgraph Federated Learning with Differentiable Auxiliary Projections
65. NeurIPS 2025Resource-Constrained Federated Continual Learning: What Does Matter?
66. NeurIPS 2025Revisiting Consensus Error: A Fine-grained Analysis of Local SGD under Second-order Data Heterogeneity
67. NeurIPS 2025Rising from Ashes: Generalized Federated Learning via Dynamic Parameter Reset
68. NeurIPS 2025Sharp Gaussian approximations for Decentralized Federated Learning
69. NeurIPS 2025Soft-consensual Federated Learning for Data Heterogeneity via Multiple Paths
70. NeurIPS 2025Streaming Federated Learning with Markovian Data
71. NeurIPS 2025Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation
72. NeurIPS 2025Tracing Back the Malicious Clients in Poisoning Attacks to Federated Learning
73. NeurIPS 2025Vertical Federated Feature Screening
74. NeurIPS 2024A Bayesian Approach for Personalized Federated Learning in Heterogeneous Settings
75. NeurIPS 2024A-FedPD: Aligning Dual-Drift is All Federated Primal-Dual Learning Needs
76. NeurIPS 2024Bridging Gaps: Federated Multi-View Clustering in Heterogeneous Hybrid Views
77. NeurIPS 2024Confusion-Resistant Federated Learning via Diffusion-Based Data Harmonization on Non-IID Data
78. NeurIPS 2024DataStealing: Steal Data from Diffusion Models in Federated Learning with Multiple Trojans
79. NeurIPS 2024Does Egalitarian Fairness Lead to Instability? The Fairness Bounds in Stable Federated Learning Under Altruistic Behaviors
80. NeurIPS 2024FACT or Fiction: Can Truthful Mechanisms Eliminate Federated Free Riding?
81. NeurIPS 2024FedAvP: Augment Local Data via Shared Policy in Federated Learning
82. NeurIPS 2024Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning
83. NeurIPS 2024Federated Ensemble-Directed Offline Reinforcement Learning
84. NeurIPS 2024Federated Graph Learning for Cross-Domain Recommendation
85. NeurIPS 2024Federated Learning over Connected Modes
86. NeurIPS 2024Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and Analysis
87. NeurIPS 2024Federated Model Heterogeneous Matryoshka Representation Learning
88. NeurIPS 2024FedGTST: Boosting Global Transferability of Federated Models via Statistics Tuning
89. NeurIPS 2024FedLPA: One-shot Federated Learning with Layer-Wise Posterior Aggregation
90. NeurIPS 2024Ferrari: Federated Feature Unlearning via Optimizing Feature Sensitivity
91. NeurIPS 2024FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Model Fusion
92. NeurIPS 2024Hierarchical Federated Learning with Multi-Timescale Gradient Correction
93. NeurIPS 2024Local Superior Soups: A Catalyst for Model Merging in Cross-Silo Federated Learning
94. NeurIPS 2024Nonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Data
95. NeurIPS 2024Parameter Disparities Dissection for Backdoor Defense in Heterogeneous Federated Learning
96. NeurIPS 2024pFedClub: Controllable Heterogeneous Model Aggregation for Personalized Federated Learning
97. NeurIPS 2024Revisiting Ensembling in One-Shot Federated Learning
98. NeurIPS 2024SPEAR: Exact Gradient Inversion of Batches in Federated Learning
99. NeurIPS 2024Taming Cross-Domain Representation Variance in Federated Prototype Learning with Heterogeneous Data Domains
100. NeurIPS 2024The Power of Extrapolation in 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。