30 citations · 41 across the 13 of their papers we have counts for
15 papers
Consensus-based Decentralized Distributed Swarm Learning with Heterogeneous Big Data
Zhuoyu Yao, Dong Yang, Yue Wang +4
Artificial intelligence increasingly relies on large-scale, distributed, and heterogeneous data collected by edge devices. However, the practice of edge intelligence remains challe…
CGFormer: A Cross-Attention Based Grid-Free Transformer for Radio Map Estimation
Haihan Nan, Emmanuel Obeng Frimpong, Zhi Tian +2
Radio map estimation (RME), which predicts wireless signal metrics at unmeasured locations from sparse measurements, has attracted growing attention as a key enabler of intelligent…
FM-RME: Foundation Model Empowered Radio Map Estimation
Dong Yang, Yue Wang, Songyang Zhang +3
Traditional radio map estimation (RME) techniques fail to capture multi-dimensional and dynamic characteristics of complex spectrum environments. Recent data-driven methods achieve…
Efficient Multi-Worker Selection based Distributed Swarm Learning via Analog Aggregation
Zhuoyu Yao, Yue Wang, Songyang Zhang +3
Recent advances in distributed learning systems have introduced effective solutions for implementing collaborative artificial intelligence techniques in wireless communication netw…
Multi-Worker Selection based Distributed Swarm Learning for Edge IoT with Non-i.i.d. Data
Zhuoyu Yao, Yue Wang, Songyang Zhang +3
Recent advances in distributed swarm learning (DSL) offer a promising paradigm for edge Internet of Things. Such advancements enhance data privacy, communication efficiency, energy…
Distributed Swarm Learning for Edge Internet of Things
Yue Wang, Zhi Tian, FXin Fan +3
The rapid growth of Internet of Things (IoT) has led to the widespread deployment of smart IoT devices at wireless edge for collaborative machine learning tasks, ushering in a new…
Dual-domain Defenses for Byzantine-resilient Decentralized Resource Allocation
Runhua Wang, Qing Ling, Zhi Tian
This paper investigates the problem of decentralized resource allocation in the presence of Byzantine attacks. Such attacks occur when an unknown number of malicious agents send ra…
Byzantine-Robust Distributed Online Learning: Taming Adversarial Participants in An Adversarial Environment
Xingrong Dong, Zhaoxian Wu, Qing Ling +1
This paper studies distributed online learning under Byzantine attacks. The performance of an online learning algorithm is often characterized by (adversarial) regret, which evalua…
Distributed Swarm Learning for Internet of Things at the Edge: Where Artificial Intelligence Meets Biological Intelligence
Yue Wang, Zhi Tian, Xin Fan +3
With the proliferation of versatile Internet of Things (IoT) services, smart IoT devices are increasingly deployed at the edge of wireless networks to perform collaborative machine…
Robust Distributed Learning Against Both Distributional Shifts and Byzantine Attacks
Guanqiang Zhou, Ping Xu, Yue Wang +1
In distributed learning systems, robustness issues may arise from two sources. On one hand, due to distributional shifts between training data and test data, the trained model coul…
QC-ODKLA: Quantized and Communication-Censored Online Decentralized Kernel Learning via Linearized ADMM
Ping Xu, Yue Wang, Xiang Chen +1
This paper focuses on online kernel learning over a decentralized network. Each agent in the network receives continuous streaming data locally and works collaboratively to learn a…
CB-DSL: Communication-efficient and Byzantine-robust Distributed Swarm Learning on Non-i.i.d. Data
Xin Fan, Yue Wang, Yan Huo +1
The valuable data collected by IoT devices in edge networks together with the resurgence of ML stimulate the latest trend of edge AI. However, recent FL methods face major challeng…
Fed2: Feature-Aligned Federated Learning
Fuxun Yu, Weishan Zhang, Zhuwei Qin +5
Federated learning learns from scattered data by fusing collaborative models from local nodes. However, the conventional coordinate-based model averaging by FedAvg ignored the rand…
BEV-SGD: Best Effort Voting SGD for Analog Aggregation Based Federated Learning against Byzantine Attackers
Xin Fan, Yue Wang, Yan Huo +1
As a promising distributed learning technology, analog aggregation based federated learning over the air (FLOA) provides high communication efficiency and privacy provisioning unde…
Heterogeneous Federated Learning
Fuxun Yu, Weishan Zhang, Zhuwei Qin +5
Federated learning learns from scattered data by fusing collaborative models from local nodes. However, due to chaotic information distribution, the model fusion may suffer from st…