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

20 papers, page 1 of 1

1. NeurIPS 2025Class-wise Balancing Data Replay for Federated Class-Incremental Learning
2. NeurIPS 2025Covariances for Free: Exploiting Mean Distributions for Training-free Federated Learning
3. NeurIPS 2025FedEL: Federated Elastic Learning for Heterogeneous Devices
4. NeurIPS 2025FedFree: Breaking Knowledge-sharing Barriers through Layer-wise Alignment in Heterogeneous Federated Learning
5. NeurIPS 2025FedGPS: Statistical Rectification Against Data Heterogeneity in Federated Learning
6. NeurIPS 2025FedRTS: Federated Robust Pruning via Combinatorial Thompson Sampling
7. NeurIPS 2025Flick: Empowering Federated Learning with Commonsense Knowledge
8. NeurIPS 2025HYPERION: Fine-Grained Hypersphere Alignment for Robust Federated Graph Learning
9. NeurIPS 2025LLM at Network Edge: A Layer-wise Efficient Federated Fine-tuning Approach
10. NeurIPS 2025On the Edge of Memorization in Diffusion Models
11. NeurIPS 2025Personalized Subgraph Federated Learning with Differentiable Auxiliary Projections
12. NeurIPS 2025Ravan: Multi-Head Low-Rank Adaptation for Federated Fine-Tuning
13. NeurIPS 2025Resource-Constrained Federated Continual Learning: What Does Matter?
14. NeurIPS 2025Rising from Ashes: Generalized Federated Learning via Dynamic Parameter Reset
15. NeurIPS 2025Soft-consensual Federated Learning for Data Heterogeneity via Multiple Paths
16. NeurIPS 2025Streaming Federated Learning with Markovian Data
17. NeurIPS 2025Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation
18. NeurIPS 2025Towards Straggler-Resilient Split Federated Learning: An Unbalanced Update Approach
19. NeurIPS 2025Transfer Learning on Edge Connecting Probability Estimation Under Graphon Model
20. NeurIPS 2025Variational Learning Finds Flatter Solutions at the Edge of Stability

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.