210 citations · 1.1k across the 95 of their papers we have counts for
9 papers · 1 filter
Over-the-Air Federated Learning and Optimization
Jingyang Zhu, Yuanming Shi, Yong Zhou +3
Federated learning (FL), as an emerging distributed machine learning paradigm, allows a mass of edge devices to collaboratively train a global model while preserving privacy. In th…
Federated Linear Bandit Learning via Over-the-Air Computation
Jiali Wang, Yuning Jiang, Xin Liu +2
In this paper, we investigate federated contextual linear bandit learning within a wireless system that comprises a server and multiple devices. Each device interacts with the envi…
Trustworthy Federated Learning via Blockchain
Zhanpeng Yang, Yuanming Shi, Yong Zhou +2
The safety-critical scenarios of artificial intelligence (AI), such as autonomous driving, Internet of Things, smart healthcare, etc., have raised critical requirements of trustwor…
Communication-Efficient Stochastic Zeroth-Order Optimization for Federated Learning
Wenzhi Fang, Ziyi Yu, Yuning Jiang +3
Federated learning (FL), as an emerging edge artificial intelligence paradigm, enables many edge devices to collaboratively train a global model without sharing their private data.…
Faster Activity and Data Detection in Massive Random Access: A Multi-armed Bandit Approach
Jialin Dong, Jun Zhang, Yuanming Shi +1
This paper investigates the grant-free random access with massive IoT devices. By embedding the data symbols in the signature sequences, joint device activity detection and data de…
A Quasi-Newton Method Based Vertical Federated Learning Framework for Logistic Regression
Kai Yang, Tao Fan, Tianjian Chen +2
Data privacy and security becomes a major concern in building machine learning models from different data providers. Federated learning shows promise by leaving data at providers l…
Learning One-hidden-layer neural networks via Provable Gradient Descent with Random Initialization
Shuhao Xia, Yuanming Shi
Although deep learning has shown its powerful performance in many applications, the mathematical principles behind neural networks are still mysterious. In this paper, we consider…
Federated Learning via Over-the-Air Computation
Kai Yang, Tao Jiang, Yuanming Shi +1
The stringent requirements for low-latency and privacy of the emerging high-stake applications with intelligent devices such as drones and smart vehicles make the cloud computing i…
An algebraic-geometric approach for linear regression without correspondences
Manolis C. Tsakiris, Liangzu Peng, Aldo Conca +3
Linear regression without correspondences is the problem of performing a linear regression fit to a dataset for which the correspondences between the independent samples and the ob…