8 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…
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…
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…
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…