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 3 / 10 页

101. NeurIPS 2026FedTrace: Generated-Content-Based Watermark Verification for Traitor Tracing in Federated Learning
102. NeurIPS 2026FedVaccine: Knowledge Recall after Spatial-Temporal Catastrophic Forgetting in Federated Continual Learning via Gradient-Based Vaccine
103. NeurIPS 2026FLoRA-Chef: Making A Good LoRA Recipe in Federated Generalization
104. NeurIPS 2026Focus on Where You Aggregate: Restricted SAM for Non-IID Federated Learning
105. NeurIPS 2026Geometry-Regularized Collapse Resistance via Consensus Enhancement for Federated Learning
106. NeurIPS 2026HERO: A Heterogeneity-Aware Benchmark Library for Federated Continual Learning
107. NeurIPS 2026Heterogeneity-aware Distillation for Federated Continual Learning
108. NeurIPS 2026LEAN: Library-Based Adaptation for Asynchronous, Federated Fine-Tuning
109. NeurIPS 2026MainFL: Intertemporal Data Valuation for Robust Auction-based Federated Learning
110. NeurIPS 2026Principled Federated Random Forests for Heterogeneous Data
111. NeurIPS 2026Rehearsal-Free Statistical Prototype Regularization for Federated Incremental Learning
112. NeurIPS 2026Reliable Federated Multi-View Learning via Conflict-Aware Evidence Calibration
113. NeurIPS 2026Reviving Stale Updates: Data-Free Knowledge Distillation for Asynchronous Federated Learning
114. NeurIPS 2026Semantic-Statistical Prior Banks for Federated Low-Shot Learning under Non-IID Clients
115. NeurIPS 2026ShadowFPT: Backdooring Federated Prompt Tuning via Shadow Triggers
116. NeurIPS 2026Stability-Enhanced Federated Learning with Accelerated Gradient
117. NeurIPS 2026SynTeX-FL: Cross-Modal Text Transfer in Federated Learning for Medical Visual Question Answering
118. NeurIPS 2026Synthetic Anchor-Assisted Prototype Alignment for Heterogeneous Federated Learning
119. NeurIPS 2026Utility-Driven Clustered Federated Learning via Class-wise Interaction Attribution
120. CVPR 2025A Simple Data Augmentation for Feature Distribution Skewed Federated Learning
121. CVPR 2025AFL: A Single-Round Analytic Approach for Federated Learning with Pre-trained Models
122. CVPR 2025CroCoDL: Cross-device Collaborative Dataset for Localization
123. CVPR 2025dFLMoE: Decentralized Federated Learning via Mixture of Experts for Medical Data Analysis
124. CVPR 2025FedAWA: Adaptive Optimization of Aggregation Weights in Federated Learning Using Client Vectors
125. CVPR 2025FedCALM: Conflict-aware Layer-wise Mitigation for Selective Aggregation in Deeper Personalized Federated Learning
126. CVPR 2025FedCS: Coreset Selection for Federated Learning
127. CVPR 2025Federated Learning with Domain Shift Eraser
128. CVPR 2025FedMIA: An Effective Membership Inference Attack Exploiting "All for One" Principle in Federated Learning
129. CVPR 2025FedSPA: Generalizable Federated Graph Learning under Homophily Heterogeneity
130. CVPR 2025Geometric Knowledge-Guided Localized Global Distribution Alignment for Federated Learning
131. CVPR 2025Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor
132. CVPR 2025HistoFS: Non-IID Histopathologic Whole Slide Image Classification via Federated Style Transfer with RoI-Preserving
133. CVPR 2025Infighting in the Dark: Multi-Label Backdoor Attack in Federated Learning
134. CVPR 2025Mind the Gap: Confidence Discrepancy Can Guide Federated Semi-Supervised Learning Across Pseudo-Mismatch
135. CVPR 2025NoT: Federated Unlearning via Weight Negation
136. CVPR 2025pFedMxF: Personalized Federated Class-Incremental Learning with Mixture of Frequency Aggregation
137. CVPR 2025Population Normalization for Federated Learning
138. CVPR 2025Unlearning through Knowledge Overwriting: Reversible Federated Unlearning via Selective Sparse Adapter
139. ICLR 2025Adaptive Gradient Clipping for Robust Federated Learning
140. ICLR 2025Can Textual Gradient Work in Federated Learning?
141. ICLR 2025Connecting Federated ADMM to Bayes
142. ICLR 2025Debiasing Federated Learning with Correlated Client Participation
143. ICLR 2025Decentralized Sporadic Federated Learning: A Unified Algorithmic Framework with Convergence Guarantees
144. ICLR 2025Decoupled Subgraph Federated Learning
145. ICLR 2025Event-Driven Online Vertical Federated Learning
146. ICLR 2025Federated Domain Generalization with Data-free On-server Matching Gradient
147. ICLR 2025Federated Few-Shot Class-Incremental Learning
148. ICLR 2025Federated Residual Low-Rank Adaptation of Large Language Models
149. ICLR 2025FedLWS: Federated Learning with Adaptive Layer-wise Weight Shrinking
150. ICLR 2025FedTMOS: Efficient One-Shot Federated Learning with Tsetlin Machine

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。