9 papers
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…
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…
Cooperative Gradient Coding
Shudi Weng, Ming Xiao, Chao Ren +1
This work studies gradient coding (GC) in the context of distributed training problems with unreliable communication. We propose cooperative GC (CoGC), a novel gradient-sharing-bas…
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…
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…
Supplementary File: Coded Cooperative Networks for Semi-Decentralized Federated Learning
Shudi Weng, Ming Xiao, Chao Ren +1
To enhance straggler resilience in federated learning (FL) systems, a semi-decentralized approach has been recently proposed, enabling collaboration between clients. Unlike the exi…
Supplementary File: Cooperative Gradient Coding for Semi-Decentralized Federated Learning
Shudi Weng, Chengxi Li, Ming Xiao +1
Stragglers' effects are known to degrade FL performance. In this paper, we investigate federated learning (FL) over wireless networks in the presence of communication stragglers, w…