1 citations · 1 across the 14 of their papers we have counts for
3 papers · 1 filter
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…