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20222026
most citedPrioritized Information Bottleneck Theoretic Framework with Distributed Online Learning for Edge Video Analytics

41 citations · 89 across the 25 of their papers we have counts for

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5 papers · 1 filter

cs.IT2026

More Interactions Make Communications More Efficient: A Directed Information Bottleneck Approach to Task-Oriented Communication

Zhirun Zheng, Yiqin Deng, Phone Lin +1

Task-oriented communication improves communication efficiency by transmitting semantic information required for downstream tasks, but may incur redundancy with previously transmitt…

cs.RO2026

Multi-Agent Embodied Autonomous Driving (MAEAD): From V2X Information Exchange to Shared World Models

Senkang Hu, Zhengru Fang, Yihang Tao +3

Autonomous driving is shifting from isolated vehicle intelligence toward multi-agent embodied systems that share perception, infer intent, and coordinate action under uncertainty.…

cs.LG2026

Federated Mixture-of-Experts Alignment on Mobile Edge Networks under Data Heterogeneity

Zihan Fang, Qianru Wang, Haonan An +5

The growing demand for on-device large language model (LLM) services on mobile edge devices has driven the adoption of Mixture-of-Experts (MoE) architectures, which scale model cap…

cs.NI2026

MLFCIL: A Multi-Level Forgetting Mitigation Framework for Federated Class-Incremental Learning in LEO Satellites

Heng Zhang, Xiaohong Deng, Sijing Duan +4

Low-Earth-orbit (LEO) satellite constellations are increasingly performing on-board computing. However, the continuous emergence of new classes under strict memory and communicatio…

cs.LG2026

HFedMoE: Resource-aware Heterogeneous Federated Learning with Mixture-of-Experts

Zihan Fang, Zheng Lin, Senkang Hu +5

While federated learning (FL) enables fine-tuning of large language models (LLMs) without compromising data privacy, the substantial size of an LLM renders on-device training impra…