Federated Learning papers, 2023–2026

Key findings

• Federated Learning fell from 137 papers in 2023 to 119 in 2026, so interest is cooling.
• NeurIPS publishes the most Federated Learning work: 157 papers from 2023 to 2026, 34% of the four-venue total.
• The fastest-growing overlap is with Low-Rank Adaptation: papers tagged with both rose from 0 in 2023 to 12 in 2026.
• Among the 5 topics under Federated and Privacy, it ranks #2 by 2026 paper count; the fastest grower in the group, Secure Aggregation, is up 1.8x versus 0.9x here.

Representative papers

Spread across years and venues so every edition shows up

• 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

All Federated Learning papers

137 papers, page 1 of 3

1. CVPR 2023Adaptive Channel Sparsity for Federated Learning Under System Heterogeneity
2. CVPR 2023Bias-Eliminating Augmentation Learning for Debiased Federated Learning
3. CVPR 2023Confidence-Aware Personalized Federated Learning via Variational Expectation Maximization
4. CVPR 2023DaFKD: Domain-Aware Federated Knowledge Distillation
5. CVPR 2023Efficient On-Device Training via Gradient Filtering
6. CVPR 2023FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning
7. CVPR 2023Federated Domain Generalization With Generalization Adjustment
8. CVPR 2023Federated Incremental Semantic Segmentation
9. CVPR 2023Federated Learning With Data-Agnostic Distribution Fusion
10. CVPR 2023FedSeg: Class-Heterogeneous Federated Learning for Semantic Segmentation
11. CVPR 2023GradMA: A Gradient-Memory-Based Accelerated Federated Learning With Alleviated Catastrophic Forgetting
12. CVPR 2023How To Prevent the Poor Performance Clients for Personalized Federated Learning?
13. CVPR 2023Learning Federated Visual Prompt in Null Space for MRI Reconstruction
14. CVPR 2023Make Landscape Flatter in Differentially Private Federated Learning
15. CVPR 2023On the Effectiveness of Partial Variance Reduction in Federated Learning With Heterogeneous Data
16. CVPR 2023Re-Thinking Federated Active Learning Based on Inter-Class Diversity
17. CVPR 2023Rethinking Federated Learning With Domain Shift: A Prototype View
18. CVPR 2023ScaleFL: Resource-Adaptive Federated Learning With Heterogeneous Clients
19. CVPR 2023STDLens: Model Hijacking-Resilient Federated Learning for Object Detection
20. CVPR 2023The Resource Problem of Using Linear Layer Leakage Attack in Federated Learning
21. ICLR 2023Better Generative Replay for Continual Federated Learning
22. ICLR 2023Bias Propagation in Federated Learning
23. ICLR 2023Combating Exacerbated Heterogeneity for Robust Models in Federated Learning
24. ICLR 2023Data-Free One-Shot Federated Learning Under Very High Statistical Heterogeneity
25. ICLR 2023Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models
26. ICLR 2023DepthFL : Depthwise Federated Learning for Heterogeneous Clients
27. ICLR 2023Effective passive membership inference attacks in federated learning against overparameterized models
28. ICLR 2023Efficient Federated Domain Translation
29. ICLR 2023EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous Data
30. ICLR 2023FedDAR: Federated Domain-Aware Representation Learning
31. ICLR 2023Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach
32. ICLR 2023Federated Learning from Small Datasets
33. ICLR 2023Federated Nearest Neighbor Machine Translation
34. ICLR 2023Federated Neural Bandits
35. ICLR 2023FedExP: Speeding Up Federated Averaging via Extrapolation
36. ICLR 2023FedFA: Federated Feature Augmentation
37. ICLR 2023FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning
38. ICLR 2023Instance-wise Batch Label Restoration via Gradients in Federated Learning
39. ICLR 2023Learning to CROSS exchange to solve min-max vehicle routing problems
40. ICLR 2023Machine Unlearning of Federated Clusters
41. ICLR 2023Meta Knowledge Condensation for Federated Learning
42. ICLR 2023MocoSFL: enabling cross-client collaborative self-supervised learning
43. ICLR 2023Multimodal Federated Learning via Contrastive Representation Ensemble
44. ICLR 2023PerFedMask: Personalized Federated Learning with Optimized Masking Vectors
45. ICLR 2023Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses
46. ICLR 2023SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication
47. ICLR 2023Test-Time Robust Personalization for Federated Learning
48. ICLR 2023Towards Addressing Label Skews in One-Shot Federated Learning
49. ICLR 2023Turning the Curse of Heterogeneity in Federated Learning into a Blessing for Out-of-Distribution Detection
50. ICML 2023Achieving Linear Speedup in Non-IID Federated Bilevel Learning

FAQ

How many Federated Learning papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

460 in total: 137 in 2023, 113 in 2024, 91 in 2025, 119 in 2026.

How many Federated Learning papers are at NeurIPS 2026?

NeurIPS 2026 has 42 Federated Learning papers, versus 31 the year before.

How many Federated Learning papers are at ICLR 2026?

ICLR 2026 has 19 Federated Learning papers, versus 18 the year before.

How many Federated Learning papers are at ICML 2026?

ICML 2026 has 37 Federated Learning papers, versus 23 the year before.

How many Federated Learning papers are at CVPR 2026?

CVPR 2026 has 21 Federated Learning papers, versus 19 the year before.

Which conference publishes the most Federated Learning papers?

NeurIPS, with 157 papers from 2023 to 2026.