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 3 of 10

101. NeurIPS 2026FedTrace: Generated-Content-Based Watermark Verification for Traitor Tracing in Federated Learning
102. NeurIPS 2026FedVaccine: Knowledge Recall after Spatial-Temporal Catastrophic Forgetting in Federated Continual Learning via Gradient-Based Vaccine
103. NeurIPS 2026FLoRA-Chef: Making A Good LoRA Recipe in Federated Generalization
104. NeurIPS 2026Focus on Where You Aggregate: Restricted SAM for Non-IID Federated Learning
105. NeurIPS 2026Geometry-Regularized Collapse Resistance via Consensus Enhancement for Federated Learning
106. NeurIPS 2026HERO: A Heterogeneity-Aware Benchmark Library for Federated Continual Learning
107. NeurIPS 2026Heterogeneity-aware Distillation for Federated Continual Learning
108. NeurIPS 2026LEAN: Library-Based Adaptation for Asynchronous, Federated Fine-Tuning
109. NeurIPS 2026MainFL: Intertemporal Data Valuation for Robust Auction-based Federated Learning
110. NeurIPS 2026Principled Federated Random Forests for Heterogeneous Data
111. NeurIPS 2026Rehearsal-Free Statistical Prototype Regularization for Federated Incremental Learning
112. NeurIPS 2026Reliable Federated Multi-View Learning via Conflict-Aware Evidence Calibration
113. NeurIPS 2026Reviving Stale Updates: Data-Free Knowledge Distillation for Asynchronous Federated Learning
114. NeurIPS 2026Semantic-Statistical Prior Banks for Federated Low-Shot Learning under Non-IID Clients
115. NeurIPS 2026ShadowFPT: Backdooring Federated Prompt Tuning via Shadow Triggers
116. NeurIPS 2026Stability-Enhanced Federated Learning with Accelerated Gradient
117. NeurIPS 2026SynTeX-FL: Cross-Modal Text Transfer in Federated Learning for Medical Visual Question Answering
118. NeurIPS 2026Synthetic Anchor-Assisted Prototype Alignment for Heterogeneous Federated Learning
119. NeurIPS 2026Utility-Driven Clustered Federated Learning via Class-wise Interaction Attribution
120. CVPR 2025A Simple Data Augmentation for Feature Distribution Skewed Federated Learning
121. CVPR 2025AFL: A Single-Round Analytic Approach for Federated Learning with Pre-trained Models
122. CVPR 2025CroCoDL: Cross-device Collaborative Dataset for Localization
123. CVPR 2025dFLMoE: Decentralized Federated Learning via Mixture of Experts for Medical Data Analysis
124. CVPR 2025FedAWA: Adaptive Optimization of Aggregation Weights in Federated Learning Using Client Vectors
125. CVPR 2025FedCALM: Conflict-aware Layer-wise Mitigation for Selective Aggregation in Deeper Personalized Federated Learning
126. CVPR 2025FedCS: Coreset Selection for Federated Learning
127. CVPR 2025Federated Learning with Domain Shift Eraser
128. CVPR 2025FedMIA: An Effective Membership Inference Attack Exploiting "All for One" Principle in Federated Learning
129. CVPR 2025FedSPA: Generalizable Federated Graph Learning under Homophily Heterogeneity
130. CVPR 2025Geometric Knowledge-Guided Localized Global Distribution Alignment for Federated Learning
131. CVPR 2025Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor
132. CVPR 2025HistoFS: Non-IID Histopathologic Whole Slide Image Classification via Federated Style Transfer with RoI-Preserving
133. CVPR 2025Infighting in the Dark: Multi-Label Backdoor Attack in Federated Learning
134. CVPR 2025Mind the Gap: Confidence Discrepancy Can Guide Federated Semi-Supervised Learning Across Pseudo-Mismatch
135. CVPR 2025NoT: Federated Unlearning via Weight Negation
136. CVPR 2025pFedMxF: Personalized Federated Class-Incremental Learning with Mixture of Frequency Aggregation
137. CVPR 2025Population Normalization for Federated Learning
138. CVPR 2025Unlearning through Knowledge Overwriting: Reversible Federated Unlearning via Selective Sparse Adapter
139. ICLR 2025Adaptive Gradient Clipping for Robust Federated Learning
140. ICLR 2025Can Textual Gradient Work in Federated Learning?
141. ICLR 2025Connecting Federated ADMM to Bayes
142. ICLR 2025Debiasing Federated Learning with Correlated Client Participation
143. ICLR 2025Decentralized Sporadic Federated Learning: A Unified Algorithmic Framework with Convergence Guarantees
144. ICLR 2025Decoupled Subgraph Federated Learning
145. ICLR 2025Event-Driven Online Vertical Federated Learning
146. ICLR 2025Federated Domain Generalization with Data-free On-server Matching Gradient
147. ICLR 2025Federated Few-Shot Class-Incremental Learning
148. ICLR 2025Federated Residual Low-Rank Adaptation of Large Language Models
149. ICLR 2025FedLWS: Federated Learning with Adaptive Layer-wise Weight Shrinking
150. ICLR 2025FedTMOS: Efficient One-Shot Federated Learning with Tsetlin Machine

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.