Federated Edge Inference papers, 2023–2026

Key findings

• Federated Edge Inference went from 43 papers in 2023 to 45 in 2026, roughly 1.0x in three years.
• ICML publishes the most Federated Edge Inference work: 60 papers from 2023 to 2026, 33% of the four-venue total.
• The fastest-growing overlap is with Edge Inference: papers tagged with both rose from 3 in 2023 to 15 in 2026.
• Among the 5 topics under Edge and IoT, it ranks #2 by 2026 paper count; the fastest grower in the group, Sensor Fusion, is up 3.8x versus 1.0x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026Does Sparse Connectivity Improve Generalization? Convolutional Networks Below the Edge of Stability
• ICML 2026Breaking the Capacity Bottleneck in Model-Heterogeneous Federated Learning via Gradual Model Restoration
• ICLR 2026Beyond Aggregation: Guiding Clients in Heterogeneous Federated Learning
• CVPR 2026E2-SCI Elastic Edge-Cloud Speculative Decoding via Credit Inertia
• NeurIPS 2025Class-wise Balancing Data Replay for Federated Class-Incremental Learning
• ICML 2025Constant Stepsize Local GD for Logistic Regression: Acceleration by Instability
• ICLR 2025Connecting Federated ADMM to Bayes
• CVPR 2025Beyond Local Sharpness: Communication-Efficient Global Sharpness-aware Minimization for Federated Learning
• NeurIPS 2024DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices
• ICML 2024Achieving Lossless Gradient Sparsification via Mapping to Alternative Space in Federated Learning
• ICLR 2024FedTrans: Client-Transparent Utility Estimation for Robust Federated Learning
• CVPR 2024FedAS: Bridging Inconsistency in Personalized Federated Learning
• NeurIPS 2023Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained Devices
• ICML 2023Achieving Linear Speedup in Non-IID Federated Bilevel Learning
• ICLR 2023Combating Exacerbated Heterogeneity for Robust Models in Federated Learning
• CVPR 2023How To Prevent the Poor Performance Clients for Personalized Federated Learning?
• NeurIPS 2026Edge of Stability Selectively Shapes Learning Across the Data Distribution
• ICML 2026Influence-Disentangled Federated Training: Learning Models That Are Easy to Unlearn
• ICLR 2026DeepAFL: Deep Analytic Federated Learning
• CVPR 2026Edge-Focused Super-Resolution for Omnidirectional Images with Spherical Geometric Augmentation

All Federated Edge Inference papers

18 papers, page 1 of 1

1. ICML 2026Benchmarking at the Edge of Comprehension
2. ICML 2026Breaking the Capacity Bottleneck in Model-Heterogeneous Federated Learning via Gradual Model Restoration
3. ICML 2026Conflicting Biases at the Edge of Stability: Norm versus Sharpness Regularization
4. ICML 2026Credibility-Aware Weighting Federated Causal Discovery for Time Series
5. ICML 2026Edge-colored Clustering in Hypergraphs: A MaxECC Approximation
6. ICML 2026FedEMoE: Improving Personalization on Heterogeneous Federated Learning via Elastic Mixture of Experts Architecture
7. ICML 2026FedFit: Federated Dynamic Sparse Training via Fisher Information scoring
8. ICML 2026FedGain: Toward Negative-Gain-Free Client Collaboration in Federated Learning
9. ICML 2026FedReLa: Imbalanced Federated Learning via Re-Labeling
10. ICML 2026Influence-Disentangled Federated Training: Learning Models That Are Easy to Unlearn
11. ICML 2026Lightweight Federated Incremental Learning via Decoupled Replay
12. ICML 2026Momentum Further Constrains Sharpness at the Edge of Stochastic Stability
13. ICML 2026No Retraining at Edge: Efficient Resource-Aware Mixed-Precision Quantization via Federated Supernet Learning
14. ICML 2026Non-Euclidean Gradient Descent Operates at the Edge of Stability
15. ICML 2026PipeSD: An Efficient Cloud-Edge Collaborative Pipeline Inference Framework with Speculative Decoding
16. ICML 2026RADE: Random Add-Drop Edge as a Regularizer
17. ICML 2026ShapCCS: Shapley-Driven Client Coreset Selection in Federated Learning
18. ICML 2026Trustworthy Federated Label Distribution Learning under Annotation Quality Disparity

FAQ

How many Federated Edge Inference papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

180 in total: 43 in 2023, 47 in 2024, 45 in 2025, 45 in 2026.

How many Federated Edge Inference papers are at NeurIPS 2026?

NeurIPS 2026 has 15 Federated Edge Inference papers, versus 20 the year before.

How many Federated Edge Inference papers are at ICLR 2026?

ICLR 2026 has 7 Federated Edge Inference papers, versus 10 the year before.

How many Federated Edge Inference papers are at ICML 2026?

ICML 2026 has 18 Federated Edge Inference papers, versus 10 the year before.

How many Federated Edge Inference papers are at CVPR 2026?

CVPR 2026 has 5 Federated Edge Inference papers, versus 5 the year before.

Which conference publishes the most Federated Edge Inference papers?

ICML, with 60 papers from 2023 to 2026.