20 papers
Coding-Enforced Robust Secure Aggregation for Federated Learning Under Unreliable Communication
Shudi Weng, Chao Ren, Yizhou Zhao +2
This work studies privacy-preserving federated learning (ppFL) under unreliable communication. In ppFL, zero-sum privacy noises enables privacy protection without sacrificing model…
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
This letter studies channel coding for over-the-air computation (AirComp). AirComp enables efficient wireless data aggregation, where computation accuracy is the key performance me…
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…