58 citations · 169 across the 39 of their papers we have counts for
6 papers · 1 filter
A Secure and Private Distributed Bayesian Federated Learning Design
Nuocheng Yang, Sihua Wang, Zhaohui Yang +3
Distributed Federated Learning (DFL) enables decentralized model training across large-scale systems without a central parameter server. However, DFL faces three critical challenge…
Contrastive Language-Image Pre-Training Model based Semantic Communication Performance Optimization
Shaoran Yang, Dongyu Wei, Hanzhi Yu +3
In this paper, a novel contrastive language-image pre-training (CLIP) model based semantic communication framework is designed. Compared to standard neural network (e.g.,convolutio…
Efficient Split Federated Learning for Large Language Models over Communication Networks
Kai Zhao, Zhaohui Yang, Ye Hu +3
Fine-tuning pre-trained large language models (LLMs) in a distributed manner poses significant challenges on resource-constrained edge networks. To address this challenge, we propo…
A Joint Gradient and Loss Based Clustered Federated Learning Design
Licheng Lin, Mingzhe Chen, Zhaohui Yang +2
In this paper, a novel clustered FL framework that enables distributed edge devices with non-IID data to independently form several clusters in a distributed manner and implement F…
Distributed Multi-agent Meta Learning for Trajectory Design in Wireless Drone Networks
Ye Hu, Mingzhe Chen, Walid Saad +2
In this paper, the problem of the trajectory design for a group of energy-constrained drones operating in dynamic wireless network environments is studied. In the considered model,…
Convergence Time Optimization for Federated Learning over Wireless Networks
Mingzhe Chen, H. Vincent Poor, Walid Saad +1
In this paper, the convergence time of federated learning (FL), when deployed over a realistic wireless network, is studied. In particular, a wireless network is considered in whic…