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

每年论文数

各会议论文数

按年份和会议统计

联邦学习论文数,按年份和会议
年份NeurIPSICLRICMLCVPR合计
202354293420137
202430234020113
20253118231991
202642193721119

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

全部联邦学习论文

共 119 篇,第 3 / 3 页

  1. NeurIPS 2026FedTrace: Generated-Content-Based Watermark Verification for Traitor Tracing in Federated Learning
  2. NeurIPS 2026FedVaccine: Knowledge Recall after Spatial-Temporal Catastrophic Forgetting in Federated Continual Learning via Gradient-Based Vaccine
  3. NeurIPS 2026FLoRA-Chef: Making A Good LoRA Recipe in Federated Generalization
  4. NeurIPS 2026Focus on Where You Aggregate: Restricted SAM for Non-IID Federated Learning
  5. NeurIPS 2026Geometry-Regularized Collapse Resistance via Consensus Enhancement for Federated Learning
  6. NeurIPS 2026HERO: A Heterogeneity-Aware Benchmark Library for Federated Continual Learning
  7. NeurIPS 2026Heterogeneity-aware Distillation for Federated Continual Learning
  8. NeurIPS 2026LEAN: Library-Based Adaptation for Asynchronous, Federated Fine-Tuning
  9. NeurIPS 2026MainFL: Intertemporal Data Valuation for Robust Auction-based Federated Learning
  10. NeurIPS 2026Principled Federated Random Forests for Heterogeneous Data
  11. NeurIPS 2026Rehearsal-Free Statistical Prototype Regularization for Federated Incremental Learning
  12. NeurIPS 2026Reliable Federated Multi-View Learning via Conflict-Aware Evidence Calibration
  13. NeurIPS 2026Reviving Stale Updates: Data-Free Knowledge Distillation for Asynchronous Federated Learning
  14. NeurIPS 2026Semantic-Statistical Prior Banks for Federated Low-Shot Learning under Non-IID Clients
  15. NeurIPS 2026ShadowFPT: Backdooring Federated Prompt Tuning via Shadow Triggers
  16. NeurIPS 2026Stability-Enhanced Federated Learning with Accelerated Gradient
  17. NeurIPS 2026SynTeX-FL: Cross-Modal Text Transfer in Federated Learning for Medical Visual Question Answering
  18. NeurIPS 2026Synthetic Anchor-Assisted Prototype Alignment for Heterogeneous Federated Learning
  19. NeurIPS 2026Utility-Driven Clustered Federated Learning via Class-wise Interaction Attribution

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"}

两分钟接入 Paperlist MCP(Claude Code、Cursor、Codex、ChatGPT)

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。

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