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 2 of 3

51. ICML 2023Anchor Sampling for Federated Learning with Partial Client Participation
52. ICML 2023Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learning
53. ICML 2023Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning Using Independent Component Analysis
54. ICML 2023Communication-Efficient Federated Hypergradient Computation via Aggregated Iterative Differentiation
55. ICML 2023Conformal Prediction for Federated Uncertainty Quantification Under Label Shift
56. ICML 2023DoCoFL: Downlink Compression for Cross-Device Federated Learning
57. ICML 2023Doubly Adversarial Federated Bandits
58. ICML 2023Fast Federated Machine Unlearning with Nonlinear Functional Theory
59. ICML 2023FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction
60. ICML 2023FedCR: Personalized Federated Learning Based on Across-Client Common Representation with Conditional Mutual Information Regularization
61. ICML 2023FedDisco: Federated Learning with Discrepancy-Aware Collaboration
62. ICML 2023Federated Adversarial Learning: A Framework with Convergence Analysis
63. ICML 2023Federated Conformal Predictors for Distributed Uncertainty Quantification
64. ICML 2023Federated Heavy Hitter Recovery under Linear Sketching
65. ICML 2023FedHPO-Bench: A Benchmark Suite for Federated Hyperparameter Optimization
66. ICML 2023GuardHFL: Privacy Guardian for Heterogeneous Federated Learning
67. ICML 2023Improving the Model Consistency of Decentralized Federated Learning
68. ICML 2023LeadFL: Client Self-Defense against Model Poisoning in Federated Learning
69. ICML 2023LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning
70. ICML 2023No One Idles: Efficient Heterogeneous Federated Learning with Parallel Edge and Server Computation
71. ICML 2023On the Convergence of Federated Averaging with Cyclic Client Participation
72. ICML 2023One-Shot Federated Conformal Prediction
73. ICML 2023Optimizing the Collaboration Structure in Cross-Silo Federated Learning
74. ICML 2023Out-of-Distribution Generalization of Federated Learning via Implicit Invariant Relationships
75. ICML 2023Personalized Federated Learning under Mixture of Distributions
76. ICML 2023Personalized Federated Learning with Inferred Collaboration Graphs
77. ICML 2023Personalized Subgraph Federated Learning
78. ICML 2023Private Federated Learning with Autotuned Compression
79. ICML 2023Revisiting Weighted Aggregation in Federated Learning with Neural Networks
80. ICML 2023SRATTA: Sample Re-ATTribution Attack of Secure Aggregation in Federated Learning.
81. ICML 2023TabLeak: Tabular Data Leakage in Federated Learning
82. ICML 2023Towards Understanding Ensemble Distillation in Federated Learning
83. ICML 2023Vertical Federated Graph Neural Network for Recommender System
84. NeurIPS 2023(Amplified) Banded Matrix Factorization: A unified approach to private training
85. NeurIPS 2023A Unified Solution for Privacy and Communication Efficiency in Vertical Federated Learning
86. NeurIPS 2023A3FL: Adversarially Adaptive Backdoor Attacks to Federated Learning
87. NeurIPS 2023Convergence Analysis of Sequential Federated Learning on Heterogeneous Data
88. NeurIPS 2023Correlation Aware Sparsified Mean Estimation Using Random Projection
89. NeurIPS 2023Cross-modal Active Complementary Learning with Self-refining Correspondence
90. NeurIPS 2023DELTA: Diverse Client Sampling for Fasting Federated Learning
91. NeurIPS 2023DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learning
92. NeurIPS 2023Dynamic Personalized Federated Learning with Adaptive Differential Privacy
93. NeurIPS 2023EvoFed: Leveraging Evolutionary Strategies for Communication-Efficient Federated Learning
94. NeurIPS 2023Fast Optimal Locally Private Mean Estimation via Random Projections
95. NeurIPS 2023Fed-GraB: Federated Long-tailed Learning with Self-Adjusting Gradient Balancer
96. NeurIPS 2023Federated Compositional Deep AUC Maximization
97. NeurIPS 2023Federated Conditional Stochastic Optimization
98. NeurIPS 2023Federated Learning via Meta-Variational Dropout
99. NeurIPS 2023Federated Learning with Bilateral Curation for Partially Class-Disjoint Data
100. NeurIPS 2023Federated Learning with Manifold Regularization and Normalized Update Reaggregation

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.