16 papers
Aggregation Alignment for Federated Learning with Mixture-of-Experts under Data Heterogeneity
Zihan Fang, Qianru Wang, Haonan An +4
Large language models (LLMs) increasingly adopt Mixture-of-Experts (MoE) architectures to scale model capacity while reducing computation. Fine-tuning these MoE-based LLMs often re…
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…
Task-Oriented Semantic Compression for Localization at the Network Edge
Zhengru Fang, Senkang Hu, Yu Guo +2
Achieving precise visual localization in GPS-limited urban environments poses significant challenges for resource-constrained mobile platforms, particularly under strict bandwidth,…
CP-uniGuard: A Unified, Probability-Agnostic, and Adaptive Framework for Malicious Agent Detection and Defense in Multi-Agent Embodied Perception Systems
Senkang Hu, Yihang Tao, Guowen Xu +5
Collaborative Perception (CP) has been shown to be a promising technique for multi-agent autonomous driving and multi-agent robotic systems, where multiple agents share their perce…
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…
Minimizing Maximum Latency of Task Offloading for Multi-UAV-assisted Maritime Search and Rescue
Shuang Qi, Bin Lin, Yiqin Deng +2
Unmanned Aerial Vehicles (UAVs) play a crucial role in Maritime Search and Rescue (MSAR), contributing to the improvement of rescue efficiency and reduction of casualties. Typicall…