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

460 papers, page 7 of 10

301. NeurIPS 2024FedAvP: Augment Local Data via Shared Policy in Federated Learning
302. NeurIPS 2024Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning
303. NeurIPS 2024Federated Ensemble-Directed Offline Reinforcement Learning
304. NeurIPS 2024Federated Graph Learning for Cross-Domain Recommendation
305. NeurIPS 2024Federated Learning over Connected Modes
306. NeurIPS 2024Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and Analysis
307. NeurIPS 2024Federated Model Heterogeneous Matryoshka Representation Learning
308. NeurIPS 2024FedGTST: Boosting Global Transferability of Federated Models via Statistics Tuning
309. NeurIPS 2024FedLPA: One-shot Federated Learning with Layer-Wise Posterior Aggregation
310. NeurIPS 2024Ferrari: Federated Feature Unlearning via Optimizing Feature Sensitivity
311. NeurIPS 2024FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Model Fusion
312. NeurIPS 2024Hierarchical Federated Learning with Multi-Timescale Gradient Correction
313. NeurIPS 2024Local Superior Soups: A Catalyst for Model Merging in Cross-Silo Federated Learning
314. NeurIPS 2024Nonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Data
315. NeurIPS 2024Parameter Disparities Dissection for Backdoor Defense in Heterogeneous Federated Learning
316. NeurIPS 2024pFedClub: Controllable Heterogeneous Model Aggregation for Personalized Federated Learning
317. NeurIPS 2024Revisiting Ensembling in One-Shot Federated Learning
318. NeurIPS 2024SPEAR: Exact Gradient Inversion of Batches in Federated Learning
319. NeurIPS 2024Taming Cross-Domain Representation Variance in Federated Prototype Learning with Heterogeneous Data Domains
320. NeurIPS 2024The Power of Extrapolation in Federated Learning
321. NeurIPS 2024The Sample-Communication Complexity Trade-off in Federated Q-Learning
322. NeurIPS 2024Towards Diverse Device Heterogeneous Federated Learning via Task Arithmetic Knowledge Integration
323. NeurIPS 2024Why Go Full? Elevating Federated Learning Through Partial Network Updates
324. CVPR 2023Adaptive Channel Sparsity for Federated Learning Under System Heterogeneity
325. CVPR 2023Bias-Eliminating Augmentation Learning for Debiased Federated Learning
326. CVPR 2023Confidence-Aware Personalized Federated Learning via Variational Expectation Maximization
327. CVPR 2023DaFKD: Domain-Aware Federated Knowledge Distillation
328. CVPR 2023Efficient On-Device Training via Gradient Filtering
329. CVPR 2023FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning
330. CVPR 2023Federated Domain Generalization With Generalization Adjustment
331. CVPR 2023Federated Incremental Semantic Segmentation
332. CVPR 2023Federated Learning With Data-Agnostic Distribution Fusion
333. CVPR 2023FedSeg: Class-Heterogeneous Federated Learning for Semantic Segmentation
334. CVPR 2023GradMA: A Gradient-Memory-Based Accelerated Federated Learning With Alleviated Catastrophic Forgetting
335. CVPR 2023How To Prevent the Poor Performance Clients for Personalized Federated Learning?
336. CVPR 2023Learning Federated Visual Prompt in Null Space for MRI Reconstruction
337. CVPR 2023Make Landscape Flatter in Differentially Private Federated Learning
338. CVPR 2023On the Effectiveness of Partial Variance Reduction in Federated Learning With Heterogeneous Data
339. CVPR 2023Re-Thinking Federated Active Learning Based on Inter-Class Diversity
340. CVPR 2023Rethinking Federated Learning With Domain Shift: A Prototype View
341. CVPR 2023ScaleFL: Resource-Adaptive Federated Learning With Heterogeneous Clients
342. CVPR 2023STDLens: Model Hijacking-Resilient Federated Learning for Object Detection
343. CVPR 2023The Resource Problem of Using Linear Layer Leakage Attack in Federated Learning
344. ICLR 2023Better Generative Replay for Continual Federated Learning
345. ICLR 2023Bias Propagation in Federated Learning
346. ICLR 2023Combating Exacerbated Heterogeneity for Robust Models in Federated Learning
347. ICLR 2023Data-Free One-Shot Federated Learning Under Very High Statistical Heterogeneity
348. ICLR 2023Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models
349. ICLR 2023DepthFL : Depthwise Federated Learning for Heterogeneous Clients
350. ICLR 2023Effective passive membership inference attacks in federated learning against overparameterized models

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.