14 citations · 20 across the 25 of their papers we have counts for
8 papers · 1 filter
To Defer or Not to Defer? Online Learning with LLMs and Congested Experts
Vasileios Saketos, William Reveillard, Alexandre Proutiere +2
Modern AI systems increasingly combine large language models (LLMs) with human experts, using the LLM to handle routine queries while deferring uncertain cases for expert review. W…
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…
Coding-Enforced Resilient and Secure Aggregation for Hierarchical Federated Learning
Shudi Weng, Ming Xiao, Mikael Skoglund
Hierarchical federated learning (HFL) has emerged as an effective paradigm to enhance link quality between clients and the server. However, ensuring model accuracy while preserving…
Land-then-transport: A Flow Matching-Based Generative Decoder for Wireless Image Transmission
Jingwen Fu, Ming Xiao, Mikael Skoglund +1
Due to strict rate and reliability demands, wireless image transmission remains difficult for both classical layered designs and joint source-channel coding (JSCC), especially unde…
Coded Robust Aggregation for Distributed Learning under Byzantine Attacks
Chengxi Li, Ming Xiao, Mikael Skoglund
In this paper, we investigate the problem of distributed learning (DL) in the presence of Byzantine attacks. For this problem, various robust bounded aggregation (RBA) rules have b…
The Larger the Merrier? Efficient Large AI Model Inference in Wireless Edge Networks
Zhonghao Lyu, Ming Xiao, Jie Xu +2
The growing demand for large artificial intelligence model (LAIM) services is driving a paradigm shift from traditional cloud-based inference to edge-based inference for low-latenc…
Heterogeneity-Aware Client Sampling for Optimal and Efficient Federated Learning
Shudi Weng, Chao Ren, Ming Xiao +1
Federated learning (FL) commonly involves clients with diverse communication and computational capabilities. Such heterogeneity can significantly distort the optimization dynamics…
Towards Quantum Federated Learning
Chao Ren, Rudai Yan, Huihui Zhu +9
Quantum Federated Learning (QFL) is an emerging interdisciplinary field that merges the principles of Quantum Computing (QC) and Federated Learning (FL), with the goal of leveragin…