1.2k citations · 16.4k across the 357 of their papers we have counts for
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Quantization-Aware Collaborative Inference for Large Embodied AI Models
Zhonghao Lyu, Ming Xiao, Mikael Skoglund +2
Large artificial intelligence models (LAIMs) are increasingly regarded as a core intelligence engine for embodied AI applications. However, the massive parameter scale and computat…
Communication-Efficient Zero-Order and First-Order Federated Learning Methods over Wireless Networks
Mohamad Assaad, Zeinab Nehme, Merouane Debbah
Federated Learning (FL) is an emerging learning framework that enables edge devices to collaboratively train ML models without sharing their local data. FL faces, however, a signif…
BAQ: Efficient Bit Allocation Quantization for Large Language Models
Chao Zhang, Li Wang, Samson Lasaulce +1
Post-training model quantization is a widely adopted technique for reducing the memory and computational costs of large language models (LLMs). However, most existing methods rely…
Emergent Communication in Multi-Agent Reinforcement Learning for Future Wireless Networks
Marwa Chafii, Salmane Naoumi, Reda Alami +3
In different wireless network scenarios, multiple network entities need to cooperate in order to achieve a common task with minimum delay and energy consumption. Future wireless ne…
Harris Hawks Feature Selection in Distributed Machine Learning for Secure IoT Environments
Neveen Hijazi, Moayad Aloqaily, Bassem Ouni +2
The development of the Internet of Things (IoT) has dramatically expanded our daily lives, playing a pivotal role in the enablement of smart cities, healthcare, and buildings. Emer…
A Comprehensive Review and a Taxonomy of Edge Machine Learning: Requirements, Paradigms, and Techniques
Wenbin Li, Hakim Hacid, Ebtesam Almazrouei +1
The union of Edge Computing (EC) and Artificial Intelligence (AI) has brought forward the Edge AI concept to provide intelligent solutions close to the end-user environment, for pr…