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

43 papers, page 1 of 1

1. CVPR 2023How To Prevent the Poor Performance Clients for Personalized Federated Learning?
2. CVPR 2023Make Landscape Flatter in Differentially Private Federated Learning
3. CVPR 2023On the Effectiveness of Partial Variance Reduction in Federated Learning With Heterogeneous Data
4. CVPR 2023Re-Thinking Federated Active Learning Based on Inter-Class Diversity
5. CVPR 2023ScaleFL: Resource-Adaptive Federated Learning With Heterogeneous Clients
6. CVPR 2023The Resource Problem of Using Linear Layer Leakage Attack in Federated Learning
7. CVPR 2023The Treasure Beneath Multiple Annotations: An Uncertainty-Aware Edge Detector
8. ICLR 2023Combating Exacerbated Heterogeneity for Robust Models in Federated Learning
9. ICLR 2023DepthFL : Depthwise Federated Learning for Heterogeneous Clients
10. ICLR 2023Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach
11. ICLR 2023Federated Learning from Small Datasets
12. ICLR 2023Federated Nearest Neighbor Machine Translation
13. ICLR 2023Federated Neural Bandits
14. ICLR 2023FedExP: Speeding Up Federated Averaging via Extrapolation
15. ICLR 2023FedFA: Federated Feature Augmentation
16. ICLR 2023Instance-wise Batch Label Restoration via Gradients in Federated Learning
17. ICLR 2023Machine Unlearning of Federated Clusters
18. ICLR 2023PerFedMask: Personalized Federated Learning with Optimized Masking Vectors
19. ICLR 2023SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication
20. ICML 2023Achieving Linear Speedup in Non-IID Federated Bilevel Learning
21. ICML 2023Communication-Efficient Federated Hypergradient Computation via Aggregated Iterative Differentiation
22. ICML 2023Doubly Adversarial Federated Bandits
23. ICML 2023Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction
24. ICML 2023FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction
25. ICML 2023Federated Adversarial Learning: A Framework with Convergence Analysis
26. ICML 2023Federated Heavy Hitter Recovery under Linear Sketching
27. ICML 2023Improving the Model Consistency of Decentralized Federated Learning
28. ICML 2023On the Convergence of Federated Averaging with Cyclic Client Participation
29. ICML 2023Personalized Federated Learning under Mixture of Distributions
30. ICML 2023Private Federated Learning with Autotuned Compression
31. ICML 2023Second-order regression models exhibit progressive sharpening to the edge of stability
32. ICML 2023TabLeak: Tabular Data Leakage in Federated Learning
33. NeurIPS 2023Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained Devices
34. NeurIPS 2023Fed-FA: Theoretically Modeling Client Data Divergence for Federated Language Backdoor Defense
35. NeurIPS 2023Federated Compositional Deep AUC Maximization
36. NeurIPS 2023Federated Learning with Bilateral Curation for Partially Class-Disjoint Data
37. NeurIPS 2023Federated Linear Bandits with Finite Adversarial Actions
38. NeurIPS 2023FedFed: Feature Distillation against Data Heterogeneity in Federated Learning
39. NeurIPS 2023FLuID: Mitigating Stragglers in Federated Learning using Invariant Dropout
40. NeurIPS 2023From Trainable Negative Depth to Edge Heterophily in Graphs
41. NeurIPS 2023Private Federated Frequency Estimation: Adapting to the Hardness of the Instance
42. NeurIPS 2023TIES-Merging: Resolving Interference When Merging Models
43. NeurIPS 2023Towards Personalized Federated Learning via Heterogeneous Model Reassembly

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.