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Towards Federated Long-Tailed Graph Learning: An Energy-Guided Dual Decoupling A

BACKGROUND
1 SIGNALFIRST DETECTED 24 June 2026
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24 Jun
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13 Apr
GNN-as-Judge: Unleashing the Power of LLMs for Graph Learning with GNN Feedback

arXiv:2604.08553v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown strong performance on text-attributed graphs (TAGs) due to their superior semantic

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27 Mar
Agentic Trust Coordination for Federated Learning through Adaptive Thresholding and Autonomous Decision Making in Sustainable and Resilient Industrial Networks

arXiv:2603.25334v1 Announce Type: new Abstract: Distributed intelligence in industrial networks increasingly integrates sensing, communication, and computation across

REPORT · WHAT · ArXiv CS.AIOPEN STORY
9 Jul
WHERE to Generate Matters: Budget-Aware Synthetic Augmentation for Label Skewed Federated Learning

arXiv:2607.06616v1 Announce Type: cross Abstract: Label skew in federated learning (FL) causes client drift and degrades global accuracy. Synthetic data augmentation can reduce

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18 Sept
FedeRICo: Federated Region-Influenced Coupling for Traffic Flow Prediction

arXiv:2609.20026v1 Announce Type: new Abstract: Urban traffic forecasting often relies on information distributed across stakeholders who may be unable to share raw data due to

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9 Apr
Feature-Aware Anisotropic Local Differential Privacy for Utility-Preserving Graph Representation Learning in Metal Additive Manufacturing

arXiv:2604.05077v1 Announce Type: cross Abstract: Metal additive manufacturing (AM) enables the fabrication of safety-critical components, but reliable quality assurance depends

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1 May
Robust Learning on Heterogeneous Graphs with Heterophily: A Graph Structure Learning Approach

arXiv:2604.27387v1 Announce Type: new Abstract: Heterogeneous graphs with heterophily have emerged as a powerful abstraction for modeling complex real-world systems, where nodes

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30 Jun
Beyond Sparse Supervision: Diffusion-Guided Learning for Few-Shot Graph Fraud Detection

arXiv:2606.28134v1 Announce Type: cross Abstract: Graph-based fraud detection is essential for safeguarding large-scale transaction systems, where undetected anomalies may lead to

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2 Apr
Task-Centric Personalized Federated Fine-Tuning of Language Models

arXiv:2604.00050v1 Announce Type: cross Abstract: Federated Learning (FL) has emerged as a promising technique for training language models on distributed and private datasets of

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