14 citations · 20 across the 25 of their papers we have counts for
11 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…
SkillCom: Decomposing LLM-based Semantic Communication into Task and Channel Aware Skills
Jingwen Fu, Ming Xiao, Mikael Skoglund
Large language models (LLMs) are increasingly used as semantic encoders and decoders in semantic communication. However, current LLM based systems mostly remain monolithic: a singl…
DriftDecode: One-Step Wireless Image Decoding via Drifting-Inspired Detail Recovery
Jingwen Fu, Ming Xiao, Mikael Skoglund
Generative receivers for wireless image transmission can improve reconstruction quality, but diffusion-based and flow-based decoding relies on iterative inference and therefore inc…
Channel-coded Over-the-Air Computation
Shudi Weng, Ming Xiao, Mikael Skoglund
AirComp enables efficient wireless data aggregation, however, its computation accuracy is vulnerable to channel impairments. As a promising solution, the role of redundancy for Air…
Perfectly Private Over-the-Air Computation
Shudi Weng, Ming Xiao, Mikael Skoglund
This paper studies a key research question: how to achieve perfect privacy in over-the-air computation (AirComp)? The problem is particularly intriguing due to a dilemma. Real-fiel…
Byzantine-Robust and Communication-Efficient Distributed Training: Compressive and Cyclic Gradient Coding
Chengxi Li, Youssef Allouah, Rachid Guerraoui +2
In this paper, we study the problem of distributed training (DT) under Byzantine attacks with communication constraints. While prior work has developed various robust aggregation r…
Biased Compression in Gradient Coding for Distributed Learning
Chengxi Li, Ming Xiao, Mikael Skoglund
Communication bottlenecks and the presence of stragglers pose significant challenges in distributed learning (DL). To deal with these challenges, recent advances leverage unbiased…
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…
On Resilient and Efficient Linear Secure Aggregation in Hierarchical Federated Learning
Shudi Weng, Xiang Zhang, Yizhou Zhao +3
In this paper, we study the fundamental limits of hierarchical secure aggregation under unreliable communication. We consider a hierarchical network where each client connects to m…
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…