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 137 篇，第 3 / 3 页

101. NeurIPS 2023Federated Linear Bandits with Finite Adversarial Actions
102. NeurIPS 2023Federated Multi-Objective Learning
103. NeurIPS 2023Federated Spectral Clustering via Secure Similarity Reconstruction
104. NeurIPS 2023FedFed: Feature Distillation against Data Heterogeneity in Federated Learning
105. NeurIPS 2023FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks
106. NeurIPS 2023FedL2P: Federated Learning to Personalize
107. NeurIPS 2023Fine-Grained Theoretical Analysis of Federated Zeroth-Order Optimization
108. NeurIPS 2023Flow: Per-instance Personalized Federated Learning
109. NeurIPS 2023FLuID: Mitigating Stragglers in Federated Learning using Invariant Dropout
110. NeurIPS 2023Global Update Tracking: A Decentralized Learning Algorithm for Heterogeneous Data
111. NeurIPS 2023IBA: Towards Irreversible Backdoor Attacks in Federated Learning
112. NeurIPS 2023Improved Communication Efficiency in Federated Natural Policy Gradient via ADMM-based Gradient Updates
113. NeurIPS 2023Incentivized Communication for Federated Bandits
114. NeurIPS 2023Is Heterogeneity Notorious? Taming Heterogeneity to Handle Test-Time Shift in Federated Learning
115. NeurIPS 2023Learning Dense Flow Field for Highly-accurate Cross-view Camera Localization
116. NeurIPS 2023Lockdown: Backdoor Defense for Federated Learning with Isolated Subspace Training
117. NeurIPS 2023Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM
118. NeurIPS 2023Momentum Provably Improves Error Feedback!
119. NeurIPS 2023Multiply Robust Federated Estimation of Targeted Average Treatment Effects
120. NeurIPS 2023One-Pass Distribution Sketch for Measuring Data Heterogeneity in Federated Learning
121. NeurIPS 2023Optimal cross-learning for contextual bandits with unknown context distributions
122. NeurIPS 2023PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning.
123. NeurIPS 2023Private Federated Frequency Estimation: Adapting to the Hardness of the Instance
124. NeurIPS 2023Prototype-based Aleatoric Uncertainty Quantification for Cross-modal Retrieval
125. NeurIPS 2023PTADisc: A Cross-Course Dataset Supporting Personalized Learning in Cold-Start Scenarios
126. NeurIPS 2023RECESS Vaccine for Federated Learning: Proactive Defense Against Model Poisoning Attacks
127. NeurIPS 2023Resilient Constrained Learning
128. NeurIPS 2023SimFBO: Towards Simple, Flexible and Communication-efficient Federated Bilevel Learning
129. NeurIPS 2023Solving a Class of Non-Convex Minimax Optimization in Federated Learning
130. NeurIPS 2023SPACE: Single-round Participant Amalgamation for Contribution Evaluation in Federated Learning
131. NeurIPS 2023Spectral Co-Distillation for Personalized Federated Learning
132. NeurIPS 2023StableFDG: Style and Attention Based Learning for Federated Domain Generalization
133. NeurIPS 2023Structured Federated Learning through Clustered Additive Modeling
134. NeurIPS 2023TIES-Merging: Resolving Interference When Merging Models
135. NeurIPS 2023Towards Personalized Federated Learning via Heterogeneous Model Reassembly
136. NeurIPS 2023Understanding How Consistency Works in Federated Learning via Stage-wise Relaxed Initialization
137. NeurIPS 2023Wyze Rule: Federated Rule Dataset for Rule Recommendation Benchmarking

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。