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Jingxian Xu

2 accepted papers

2025

HFedPFS: Heterogeneous Federated Learning with Personalized Data Feature Sharing

ICASSP 2025accepted

Federated learning (FL) is a distributed machine learning technique enabling multiple clients to jointly train a global model while preserving the privacy of their non-IID (non-independent and identically) data. However, traditional FL approaches require clients to use the same model structure as th…

Cited by 0SourceScholar
2025

TwT: Thinking without Tokens by Habitual Reasoning Distillation with Multi-Teachers’ Guidance

EMNLP 2025

Large Language Models (LLMs) have made significant strides in problem-solving by incorporating reasoning processes. However, this enhanced reasoning capability results in an increased number of output tokens during inference, leading to higher computational costs. To address this challenge, we propo

Cited by 0SourcePDFScholar