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 5 of 10

201. NeurIPS 2025Personalized Subgraph Federated Learning with Differentiable Auxiliary Projections
202. NeurIPS 2025Resource-Constrained Federated Continual Learning: What Does Matter?
203. NeurIPS 2025Revisiting Consensus Error: A Fine-grained Analysis of Local SGD under Second-order Data Heterogeneity
204. NeurIPS 2025Rising from Ashes: Generalized Federated Learning via Dynamic Parameter Reset
205. NeurIPS 2025Sharp Gaussian approximations for Decentralized Federated Learning
206. NeurIPS 2025Soft-consensual Federated Learning for Data Heterogeneity via Multiple Paths
207. NeurIPS 2025Streaming Federated Learning with Markovian Data
208. NeurIPS 2025Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation
209. NeurIPS 2025Tracing Back the Malicious Clients in Poisoning Attacks to Federated Learning
210. NeurIPS 2025Vertical Federated Feature Screening
211. CVPR 2024An Aggregation-Free Federated Learning for Tackling Data Heterogeneity
212. CVPR 2024Communication-Efficient Federated Learning with Accelerated Client Gradient
213. CVPR 2024Decentralized Directed Collaboration for Personalized Federated Learning
214. CVPR 2024Device-Wise Federated Network Pruning
215. CVPR 2024FedAS: Bridging Inconsistency in Personalized Federated Learning
216. CVPR 2024Federated Generalized Category Discovery
217. CVPR 2024Federated Online Adaptation for Deep Stereo
218. CVPR 2024FedHCA2: Towards Hetero-Client Federated Multi-Task Learning
219. CVPR 2024FedMef: Towards Memory-efficient Federated Dynamic Pruning
220. CVPR 2024FedSelect: Personalized Federated Learning with Customized Selection of Parameters for Fine-Tuning
221. CVPR 2024FedSOL: Stabilized Orthogonal Learning with Proximal Restrictions in Federated Learning
222. CVPR 2024FLHetBench: Benchmarking Device and State Heterogeneity in Federated Learning
223. CVPR 2024Mixed-Precision Quantization for Federated Learning on Resource-Constrained Heterogeneous Devices
224. CVPR 2024Motion Blur Decomposition with Cross-shutter Guidance
225. CVPR 2024Relaxed Contrastive Learning for Federated Learning
226. CVPR 2024Rethinking the Representation in Federated Unsupervised Learning with Non-IID Data
227. CVPR 2024Revamping Federated Learning Security from a Defender's Perspective: A Unified Defense with Homomorphic Encrypted Data Space
228. CVPR 2024Text-Enhanced Data-free Approach for Federated Class-Incremental Learning
229. CVPR 2024Towards Efficient Replay in Federated Incremental Learning
230. CVPR 2024Traceable Federated Continual Learning
231. ICLR 2024A Lightweight Method for Tackling Unknown Participation Statistics in Federated Averaging
232. ICLR 2024Accurate Forgetting for Heterogeneous Federated Continual Learning
233. ICLR 2024Adaptive Federated Learning with Auto-Tuned Clients
234. ICLR 2024Backdoor Federated Learning by Poisoning Backdoor-Critical Layers
235. ICLR 2024Constructing Adversarial Examples for Vertical Federated Learning: Optimal Client Corruption through Multi-Armed Bandit
236. ICLR 2024FedCDA: Federated Learning with Cross-rounds Divergence-aware Aggregation
237. ICLR 2024FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices Using a Computing Power-Aware Scheduler
238. ICLR 2024Federated Orthogonal Training: Mitigating Global Catastrophic Forgetting in Continual Federated Learning
239. ICLR 2024Federated Recommendation with Additive Personalization
240. ICLR 2024Federated Wasserstein Distance
241. ICLR 2024FedInverse: Evaluating Privacy Leakage in Federated Learning
242. ICLR 2024FedTrans: Client-Transparent Utility Estimation for Robust Federated Learning
243. ICLR 2024FedWon: Triumphing Multi-domain Federated Learning Without Normalization
244. ICLR 2024Heterogeneous Personalized Federated Learning by Local-Global Updates Mixing via Convergence Rate
245. ICLR 2024Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learning
246. ICLR 2024Incentivized Truthful Communication for Federated Bandits
247. ICLR 2024Internal Cross-layer Gradients for Extending Homogeneity to Heterogeneity in Federated Learning
248. ICLR 2024Learning Personalized Causally Invariant Representations for Heterogeneous Federated Clients
249. ICLR 2024On Differentially Private Federated Linear Contextual Bandits
250. ICLR 2024PeFLL: Personalized Federated Learning by Learning to Learn

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.