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

54 papers, page 1 of 2

1. NeurIPS 2023(Amplified) Banded Matrix Factorization: A unified approach to private training
2. NeurIPS 2023A Unified Solution for Privacy and Communication Efficiency in Vertical Federated Learning
3. NeurIPS 2023A3FL: Adversarially Adaptive Backdoor Attacks to Federated Learning
4. NeurIPS 2023Convergence Analysis of Sequential Federated Learning on Heterogeneous Data
5. NeurIPS 2023Correlation Aware Sparsified Mean Estimation Using Random Projection
6. NeurIPS 2023Cross-modal Active Complementary Learning with Self-refining Correspondence
7. NeurIPS 2023DELTA: Diverse Client Sampling for Fasting Federated Learning
8. NeurIPS 2023DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learning
9. NeurIPS 2023Dynamic Personalized Federated Learning with Adaptive Differential Privacy
10. NeurIPS 2023EvoFed: Leveraging Evolutionary Strategies for Communication-Efficient Federated Learning
11. NeurIPS 2023Fast Optimal Locally Private Mean Estimation via Random Projections
12. NeurIPS 2023Fed-GraB: Federated Long-tailed Learning with Self-Adjusting Gradient Balancer
13. NeurIPS 2023Federated Compositional Deep AUC Maximization
14. NeurIPS 2023Federated Conditional Stochastic Optimization
15. NeurIPS 2023Federated Learning via Meta-Variational Dropout
16. NeurIPS 2023Federated Learning with Bilateral Curation for Partially Class-Disjoint Data
17. NeurIPS 2023Federated Learning with Manifold Regularization and Normalized Update Reaggregation
18. NeurIPS 2023Federated Linear Bandits with Finite Adversarial Actions
19. NeurIPS 2023Federated Multi-Objective Learning
20. NeurIPS 2023Federated Spectral Clustering via Secure Similarity Reconstruction
21. NeurIPS 2023FedFed: Feature Distillation against Data Heterogeneity in Federated Learning
22. NeurIPS 2023FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks
23. NeurIPS 2023FedL2P: Federated Learning to Personalize
24. NeurIPS 2023Fine-Grained Theoretical Analysis of Federated Zeroth-Order Optimization
25. NeurIPS 2023Flow: Per-instance Personalized Federated Learning
26. NeurIPS 2023FLuID: Mitigating Stragglers in Federated Learning using Invariant Dropout
27. NeurIPS 2023Global Update Tracking: A Decentralized Learning Algorithm for Heterogeneous Data
28. NeurIPS 2023IBA: Towards Irreversible Backdoor Attacks in Federated Learning
29. NeurIPS 2023Improved Communication Efficiency in Federated Natural Policy Gradient via ADMM-based Gradient Updates
30. NeurIPS 2023Incentivized Communication for Federated Bandits
31. NeurIPS 2023Is Heterogeneity Notorious? Taming Heterogeneity to Handle Test-Time Shift in Federated Learning
32. NeurIPS 2023Learning Dense Flow Field for Highly-accurate Cross-view Camera Localization
33. NeurIPS 2023Lockdown: Backdoor Defense for Federated Learning with Isolated Subspace Training
34. NeurIPS 2023Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM
35. NeurIPS 2023Momentum Provably Improves Error Feedback!
36. NeurIPS 2023Multiply Robust Federated Estimation of Targeted Average Treatment Effects
37. NeurIPS 2023One-Pass Distribution Sketch for Measuring Data Heterogeneity in Federated Learning
38. NeurIPS 2023Optimal cross-learning for contextual bandits with unknown context distributions
39. NeurIPS 2023PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning.
40. NeurIPS 2023Private Federated Frequency Estimation: Adapting to the Hardness of the Instance
41. NeurIPS 2023Prototype-based Aleatoric Uncertainty Quantification for Cross-modal Retrieval
42. NeurIPS 2023PTADisc: A Cross-Course Dataset Supporting Personalized Learning in Cold-Start Scenarios
43. NeurIPS 2023RECESS Vaccine for Federated Learning: Proactive Defense Against Model Poisoning Attacks
44. NeurIPS 2023Resilient Constrained Learning
45. NeurIPS 2023SimFBO: Towards Simple, Flexible and Communication-efficient Federated Bilevel Learning
46. NeurIPS 2023Solving a Class of Non-Convex Minimax Optimization in Federated Learning
47. NeurIPS 2023SPACE: Single-round Participant Amalgamation for Contribution Evaluation in Federated Learning
48. NeurIPS 2023Spectral Co-Distillation for Personalized Federated Learning
49. NeurIPS 2023StableFDG: Style and Attention Based Learning for Federated Domain Generalization
50. NeurIPS 2023Structured Federated Learning through Clustered Additive Modeling

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.