14 citations · 20 across the 25 of their papers we have counts for
7 papers · 1 filter
Robust Multi-modal Task-oriented Communications with Redundancy-aware Representations
Jingwen Fu, Ming Xiao, Zhonghao Lyu +2
Semantic communications for multi-modal data can transmit task-relevant information efficiently over noisy and bandwidth-limited channels. However, a key challenge is to simultaneo…
Computation-resource-efficient Task-oriented Communications
Jingwen Fu, Ming Xiao, Chao Ren +1
The rapid development of deep-learning enabled task-oriented communications (TOC) significantly shifts the paradigm of wireless communications. However, the high computation demand…
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…
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…