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

119 papers, page 2 of 3

51. ICML 2026Federated Graph Learning via Structure-Aware Fusion Using a Kalman Framework with Learnable Dynamics
52. ICML 2026Federated Multi-view Clustering for Remote Sensing Data
53. ICML 2026FedFit: Federated Dynamic Sparse Training via Fisher Information scoring
54. ICML 2026FedGain: Toward Negative-Gain-Free Client Collaboration in Federated Learning
55. ICML 2026FedHera: Towards Drift-Resilient Federated Fine-tuning with Heterogeneous Resources
56. ICML 2026FedHPro: Federated Hyper-Prototype Learning via Gradient Matching
57. ICML 2026FedQueue: Queue-Aware Federated Learning for Cross-Facility HPC Training
58. ICML 2026FedReLa: Imbalanced Federated Learning via Re-Labeling
59. ICML 2026FedRGL: Robust Federated Graph Learning under Label Noise
60. ICML 2026FedRot-LoRA: Mitigating Rotational Misalignment in Federated LoRA
61. ICML 2026FedScar: Correcting Geometric Bias for Flatness-Consistent Federated Learning
62. ICML 2026FedSDR: Federated Self-Distillation with Rectification
63. ICML 2026FedSSM: State Space Model-based Proactive Inference for Heterogeneous Multimodal Federated Learning
64. ICML 2026FedUSD: Unbiased Synthetic Data for Federated Learning
65. ICML 2026FedVeer: Self-Adaptive Skew Estimation for Robust Federated Learning
66. ICML 2026GeoEvo: Identity-Aware Potential Game with Geometric Evolution for Personalized Multimodal Federated Learning
67. ICML 2026Influence-Disentangled Federated Training: Learning Models That Are Easy to Unlearn
68. ICML 2026Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels
69. ICML 2026Lightweight Federated Incremental Learning via Decoupled Replay
70. ICML 2026One-Shot Weighted Ensemble Estimation for Federated Quantile Regression: Optimal Statistical Guarantees under Heterogeneous Structured Data
71. ICML 2026Prototype-guided Bilateral Alignment Multimodal Federated Learning
72. ICML 2026RC-FCL: Combating Asynchronous Concept Drift in Federated Continual Learning via Retrospective Calibration
73. ICML 2026SFedPO: Streaming Federated Learning with a Prediction Oracle under Temporal Shifts
74. ICML 2026ShapCCS: Shapley-Driven Client Coreset Selection in Federated Learning
75. ICML 2026Towards Rule-Based Knowledge Sharing in Federated Learning
76. ICML 2026Trustworthy Federated Label Distribution Learning under Annotation Quality Disparity
77. ICML 2026Weaving in the Clouds: Achieving Synergistic Collaboration among LLM Agents via Federated Learning
78. NeurIPS 2026A typed tensor language for federated learning
79. NeurIPS 2026Align Before Aggregation: Basis-Consistent Federated LoRA under Heterogeneous Ranks
80. NeurIPS 2026Aligned LoRA Updates via Intrinsic Geometry for Federated Low-Rank Adaptation
81. NeurIPS 2026ASAP: Fast Adaptive Sliding Agnostic Poisoning Attack on Federated Learning
82. NeurIPS 2026Bayes-pFCL:Bayesian Personalized Federated Continual Learning
83. NeurIPS 2026Complexity-Aware LoRA Aggregation for Modality-Heterogeneous Federated Person Re-identification
84. NeurIPS 2026Consistency-Verified Backdoor Defense for Federated Graph Learning via Cross-Layer Drift
85. NeurIPS 2026Crossing the Validation crisis: Cross-validation reduces benchmarking variance surprisingly well
86. NeurIPS 2026Dynamic Spectral Federated Graph-Level Clustering
87. NeurIPS 2026Exploration via Exploitation: The Blessing of Reward Diversity in Personalized Federated RL
88. NeurIPS 2026Faster Rates For Federated Variational Inequalities
89. NeurIPS 2026FAUST: Federated Asynchronous Update with Staggered Timescales for Low-Communication Foundation Model Training
90. NeurIPS 2026Fed-AGA: An Anchor Graph Alignment Framework for Federated Unaligned Multi-view Clustering
91. NeurIPS 2026FedAdaVR: Adaptive Variance Reduction for Robust Federated Learning under Limited Client Participation
92. NeurIPS 2026FedCF: Fair Federated Conformal Prediction
93. NeurIPS 2026Federated Graph Learning with Local Message Compensation
94. NeurIPS 2026Federated Learning by Utility-Constrained Stochastic Aggregation for Improving Rational Participation
95. NeurIPS 2026Federated Logic Gate Networks via Boolean Feature Selection
96. NeurIPS 2026FedLoVA: Value-Only Aggregation for Federated LoRA Fine-Tuning of Large Language Models
97. NeurIPS 2026FedPeel: Peeling Stabilized Layers for Robust Heterogeneous Federated Learning
98. NeurIPS 2026FedReCall: Recalling Client-Specific Directions in Federated LoRA Fine-tuning
99. NeurIPS 2026FedSEM: Mitigating Cross-Client Evidence Drift in Federated Multiple Instance Learning
100. NeurIPS 2026FedSOUL: Federated Continual Unlearning via Spectral Orthogonality

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.