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Author

Yujie Zhou

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#machine learning Open access Oct 2025

Distributed Information Bottleneck Theory for Multi-Modal Task-Aware Semantic Communication

This work proposes a novel task-aware distributed information bottleneck (TADIB) framework, which quantifies the contribution of any set of modalities to given tasks, and designs a practical coding scheme that intelligently selects and compresses only the most task-relevant modalities at the transmitter.

Yu-Jie Zhou, Cheng Peng, Ru-Long Wang et al. · 3 citations

Fast Federated Learning via Imprecise Correction in Unreliable Wireless Networks

Due to data heterogeneity and parameter transmission failure, federated learning (FL) usually suffers performance degradation in wireless networks. To address this issue, a novel federated Learning via reusing historical Information (FedRe) is proposed for convergence acceleration and performance enhancement without an...

Juzhen Wang, Zhidu Li, Dongshen Luo et al. · 0 citations

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