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 113 篇，第 2 / 3 页

51. ICML 2024Causally Motivated Personalized Federated Invariant Learning with Shortcut-Averse Information-Theoretic Regularization
52. ICML 2024Certifiably Byzantine-Robust Federated Conformal Prediction
53. ICML 2024Clustered Federated Learning via Gradient-based Partitioning
54. ICML 2024Decomposable Submodular Maximization in Federated Setting
55. ICML 2024Effective Federated Graph Matching
56. ICML 2024Fair Federated Learning via the Proportional Veto Core
57. ICML 2024FedBAT: Communication-Efficient Federated Learning via Learnable Binarization
58. ICML 2024FedBPT: Efficient Federated Black-box Prompt Tuning for Large Language Models
59. ICML 2024Federated Combinatorial Multi-Agent Multi-Armed Bandits
60. ICML 2024Federated Continual Learning via Prompt-based Dual Knowledge Transfer
61. ICML 2024Federated Full-Parameter Tuning of Billion-Sized Language Models with Communication Cost under 18 Kilobytes
62. ICML 2024Federated Neuro-Symbolic Learning
63. ICML 2024Federated Representation Learning in the Under-Parameterized Regime
64. ICML 2024Federated Self-Explaining GNNs with Anti-shortcut Augmentations
65. ICML 2024FedLMT: Tackling System Heterogeneity of Federated Learning via Low-Rank Model Training with Theoretical Guarantees
66. ICML 2024FedMBridge: Bridgeable Multimodal Federated Learning
67. ICML 2024FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clustering
68. ICML 2024FedREDefense: Defending against Model Poisoning Attacks for Federated Learning using Model Update Reconstruction Error
69. ICML 2024FedSC: Provable Federated Self-supervised Learning with Spectral Contrastive Objective over Non-i.i.d. Data
70. ICML 2024Improved Modelling of Federated Datasets using Mixtures-of-Dirichlet-Multinomials
71. ICML 2024Lessons from Generalization Error Analysis of Federated Learning: You May Communicate Less Often!
72. ICML 2024Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimization
73. ICML 2024Momentum for the Win: Collaborative Federated Reinforcement Learning across Heterogeneous Environments
74. ICML 2024Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning
75. ICML 2024PrE-Text: Training Language Models on Private Federated Data in the Age of LLMs
76. ICML 2024Provable Benefits of Local Steps in Heterogeneous Federated Learning for Neural Networks: A Feature Learning Perspective
77. ICML 2024Pursuing Overall Welfare in Federated Learning through Sequential Decision Making
78. ICML 2024Ranking-based Client Imitation Selection for Efficient Federated Learning
79. ICML 2024Recovering Labels from Local Updates in Federated Learning
80. ICML 2024Recurrent Early Exits for Federated Learning with Heterogeneous Clients
81. ICML 2024Rethinking the Flat Minima Searching in Federated Learning
82. ICML 2024Self-Driven Entropy Aggregation for Byzantine-Robust Heterogeneous Federated Learning
83. ICML 2024Understanding Server-Assisted Federated Learning in the Presence of Incomplete Client Participation
84. NeurIPS 2024A Bayesian Approach for Personalized Federated Learning in Heterogeneous Settings
85. NeurIPS 2024A-FedPD: Aligning Dual-Drift is All Federated Primal-Dual Learning Needs
86. NeurIPS 2024Bridging Gaps: Federated Multi-View Clustering in Heterogeneous Hybrid Views
87. NeurIPS 2024Confusion-Resistant Federated Learning via Diffusion-Based Data Harmonization on Non-IID Data
88. NeurIPS 2024DataStealing: Steal Data from Diffusion Models in Federated Learning with Multiple Trojans
89. NeurIPS 2024Does Egalitarian Fairness Lead to Instability? The Fairness Bounds in Stable Federated Learning Under Altruistic Behaviors
90. NeurIPS 2024FACT or Fiction: Can Truthful Mechanisms Eliminate Federated Free Riding?
91. NeurIPS 2024FedAvP: Augment Local Data via Shared Policy in Federated Learning
92. NeurIPS 2024Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning
93. NeurIPS 2024Federated Ensemble-Directed Offline Reinforcement Learning
94. NeurIPS 2024Federated Graph Learning for Cross-Domain Recommendation
95. NeurIPS 2024Federated Learning over Connected Modes
96. NeurIPS 2024Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and Analysis
97. NeurIPS 2024Federated Model Heterogeneous Matryoshka Representation Learning
98. NeurIPS 2024FedGTST: Boosting Global Transferability of Federated Models via Statistics Tuning
99. NeurIPS 2024FedLPA: One-shot Federated Learning with Layer-Wise Posterior Aggregation
100. NeurIPS 2024Ferrari: Federated Feature Unlearning via Optimizing Feature Sensitivity

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。