联邦学习论文趋势 2023–2026
跨设备联邦
统计范围：NeurIPS、ICLR、ICML、CVPR 四个会议，2023–2026 年，共 460 篇被归入该主题的论文。
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。
各会议论文数
按年份和会议统计
| 年份 | NeurIPS | ICLR | ICML | CVPR | 合计 |
|---|---|---|---|---|---|
| 2023 | 54 | 29 | 34 | 20 | 137 |
| 2024 | 30 | 23 | 40 | 20 | 113 |
| 2025 | 31 | 18 | 23 | 19 | 91 |
| 2026 | 42 | 19 | 37 | 21 | 119 |
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 23 篇，第 1 / 1 页
- ICLR 2024A Lightweight Method for Tackling Unknown Participation Statistics in Federated Averaging
- ICLR 2024Accurate Forgetting for Heterogeneous Federated Continual Learning
- ICLR 2024Adaptive Federated Learning with Auto-Tuned Clients
- ICLR 2024Backdoor Federated Learning by Poisoning Backdoor-Critical Layers
- ICLR 2024Constructing Adversarial Examples for Vertical Federated Learning: Optimal Client Corruption through Multi-Armed Bandit
- ICLR 2024FedCDA: Federated Learning with Cross-rounds Divergence-aware Aggregation
- ICLR 2024FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices Using a Computing Power-Aware Scheduler
- ICLR 2024Federated Orthogonal Training: Mitigating Global Catastrophic Forgetting in Continual Federated Learning
- ICLR 2024Federated Recommendation with Additive Personalization
- ICLR 2024Federated Wasserstein Distance
- ICLR 2024FedInverse: Evaluating Privacy Leakage in Federated Learning
- ICLR 2024FedTrans: Client-Transparent Utility Estimation for Robust Federated Learning
- ICLR 2024FedWon: Triumphing Multi-domain Federated Learning Without Normalization
- ICLR 2024Heterogeneous Personalized Federated Learning by Local-Global Updates Mixing via Convergence Rate
- ICLR 2024Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learning
- ICLR 2024Incentivized Truthful Communication for Federated Bandits
- ICLR 2024Internal Cross-layer Gradients for Extending Homogeneity to Heterogeneity in Federated Learning
- ICLR 2024Learning Personalized Causally Invariant Representations for Heterogeneous Federated Clients
- ICLR 2024On Differentially Private Federated Linear Contextual Bandits
- ICLR 2024PeFLL: Personalized Federated Learning by Learning to Learn
- ICLR 2024Robust Training of Federated Models with Extremely Label Deficiency
- ICLR 2024Stochastic Controlled Averaging for Federated Learning with Communication Compression
- ICLR 2024Tackling the Data Heterogeneity in Asynchronous Federated Learning with Cached Update Calibration
在 Claude、Cursor 里直接问这个趋势
接上 Paperlist MCP 后，写综述、挑方向或准备投稿时，不用离开对话框就能拿到这页的数字：每年各会议的论文数、增长倍数、涨得最快的子方向和代表论文，AI 会按你的问题整理。
可以直接复制这句话问 AI
用 paperlist 查联邦学习在 NeurIPS 2023–2026 的论文趋势，哪一年涨得最快？
AI 会调用的工具：get_topic_stats {"topic": "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。