Wenke Huang

Reasoning Self-Evaluation via Trajectory Dynamics Modeling

ThanoRA: Task Heterogeneity-Aware Multi-Task Low-Rank Adaptation

AURA: Structural and Semantic Calibration for Robust Federated Graph Learning

FedSDR: Federated Graph Learning with Structural Noise Detection and Reconstruction

Diagnose, Localize, Align: A Full-Stack Framework for Reliable LLM Multi-Agent Systems under Instruction Conflicts

MAPO: MIXED ADVANTAGE POLICY OPTIMIZATION

Catch Your Emotion: Sharpening Emotion Perception in Multimodal Large Language Models

DKDR: Dynamic Knowledge Distillation for Reliability in Federated Learning

Unbiased Prototype Consistency Learning for Multi-Modal and Multi-Task Object Re-Identification

GHOST: Generalizable One-Shot Federated Graph Learning with Proxy-Based Topology Knowledge Retention

Energy-based Backdoor Defense Against Federated Graph Learning

HYPERION: Fine-Grained Hypersphere Alignment for Robust Federated Graph Learning

Learn from Downstream and Be Yourself in Multimodal Large Language Models Fine-Tuning

Does One-shot Give the Best Shot? Mitigating Model Inconsistency in One-shot Federated Learning

$S^2$FGL: Spatial Spectral Federated Graph Learning

OASIS: One-Shot Federated Graph Learning via Wasserstein Assisted Knowledge Integration

Rethinking Fair Federated Learning from Parameter and Client View

Backdoor Cleaning without External Guidance in MLLM Fine-tuning

Multi-order Orchestrated Curriculum Distillation for Model-Heterogeneous Federated Graph Learning

Be Confident: Uncovering Overfitting in MLLM Multi-Task Tuning

Federated Disentangled Tuning with Textual Prior Decoupling and Visual Dynamic Adaptation

SPMC: Self-Purifying Federated Backdoor Defense via Margin Contribution

EAGLES: Towards Effective, Efficient, and Economical Federated Graph Learning via Unified Sparsification

Spiking Meets Attention: Efficient Remote Sensing Image Super-Resolution with Attention Spiking Neural Networks

FedPHA: Federated Prompt Learning for Heterogeneous Client Adaptation

Splitting with Importance-aware Updating for Heterogeneous Federated Learning with Large Language Models

MOTION: Multi-Sculpt Evolutionary Coarsening for Federated Continual Graph Learning

Divide And Conquer: Efficiently Decoupling Consensus And Divergence For Federated Large Language Model Fine-Tuning