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

180 papers, page 2 of 4

51. ICLR 2025Can Textual Gradient Work in Federated Learning?
52. ICLR 2025Connecting Federated ADMM to Bayes
53. ICLR 2025Debiasing Federated Learning with Correlated Client Participation
54. ICLR 2025Decoupled Subgraph Federated Learning
55. ICLR 2025Enhancing Clustered Federated Learning: Integration of Strategies and Improved Methodologies
56. ICLR 2025FedLWS: Federated Learning with Adaptive Layer-wise Weight Shrinking
57. ICLR 2025Grokking at the Edge of Numerical Stability
58. ICLR 2025Learning Dynamics of Deep Matrix Factorization Beyond the Edge of Stability
59. ICLR 2025PRISM: Privacy-Preserving Improved Stochastic Masking for Federated Generative Models
60. ICLR 2025Unlocking the Potential of Model Calibration in Federated Learning
61. ICML 2025Constant Stepsize Local GD for Logistic Regression: Acceleration by Instability
62. ICML 2025Edge-Colored Clustering in Hypergraphs: Beyond Minimizing Unsatisfied Edges
63. ICML 2025FedECADO: A Dynamical System Model of Federated Learning
64. ICML 2025Federated Node-Level Clustering Network with Cross-Subgraph Link Mending
65. ICML 2025FedSSI: Rehearsal-Free Continual Federated Learning with Synergistic Synaptic Intelligence
66. ICML 2025Grokking at the Edge of Linear Separability
67. ICML 2025Hybrid Batch Normalisation: Resolving the Dilemma of Batch Normalisation in Federated Learning
68. ICML 2025Less is More: Federated Graph Learning with Alleviating Topology Heterogeneity from A Causal Perspective
69. ICML 2025NTK-DFL: Enhancing Decentralized Federated Learning in Heterogeneous Settings via Neural Tangent Kernel
70. ICML 2025Widening the Network Mitigates the Impact of Data Heterogeneity on FedAvg
71. NeurIPS 2025Class-wise Balancing Data Replay for Federated Class-Incremental Learning
72. NeurIPS 2025Covariances for Free: Exploiting Mean Distributions for Training-free Federated Learning
73. NeurIPS 2025FedEL: Federated Elastic Learning for Heterogeneous Devices
74. NeurIPS 2025FedFree: Breaking Knowledge-sharing Barriers through Layer-wise Alignment in Heterogeneous Federated Learning
75. NeurIPS 2025FedGPS: Statistical Rectification Against Data Heterogeneity in Federated Learning
76. NeurIPS 2025FedRTS: Federated Robust Pruning via Combinatorial Thompson Sampling
77. NeurIPS 2025Flick: Empowering Federated Learning with Commonsense Knowledge
78. NeurIPS 2025HYPERION: Fine-Grained Hypersphere Alignment for Robust Federated Graph Learning
79. NeurIPS 2025LLM at Network Edge: A Layer-wise Efficient Federated Fine-tuning Approach
80. NeurIPS 2025On the Edge of Memorization in Diffusion Models
81. NeurIPS 2025Personalized Subgraph Federated Learning with Differentiable Auxiliary Projections
82. NeurIPS 2025Ravan: Multi-Head Low-Rank Adaptation for Federated Fine-Tuning
83. NeurIPS 2025Resource-Constrained Federated Continual Learning: What Does Matter?
84. NeurIPS 2025Rising from Ashes: Generalized Federated Learning via Dynamic Parameter Reset
85. NeurIPS 2025Soft-consensual Federated Learning for Data Heterogeneity via Multiple Paths
86. NeurIPS 2025Streaming Federated Learning with Markovian Data
87. NeurIPS 2025Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation
88. NeurIPS 2025Towards Straggler-Resilient Split Federated Learning: An Unbalanced Update Approach
89. NeurIPS 2025Transfer Learning on Edge Connecting Probability Estimation Under Graphon Model
90. NeurIPS 2025Variational Learning Finds Flatter Solutions at the Edge of Stability
91. CVPR 20243D Neural Edge Reconstruction
92. CVPR 2024An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated Learning
93. CVPR 2024Communication-Efficient Federated Learning with Accelerated Client Gradient
94. CVPR 2024Device-Wise Federated Network Pruning
95. CVPR 2024DiPrompT: Disentangled Prompt Tuning for Multiple Latent Domain Generalization in Federated Learning
96. CVPR 2024FedAS: Bridging Inconsistency in Personalized Federated Learning
97. CVPR 2024FedSOL: Stabilized Orthogonal Learning with Proximal Restrictions in Federated Learning
98. CVPR 2024Mixed-Precision Quantization for Federated Learning on Resource-Constrained Heterogeneous Devices
99. CVPR 2024Relaxed Contrastive Learning for Federated Learning
100. CVPR 2024Revamping Federated Learning Security from a Defender's Perspective: A Unified Defense with Homomorphic Encrypted Data Space

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.