2 citations · 4 across the 5 of their papers we have counts for
6 papers
Over-the-Air Federated Learning in Heterogeneous Mobile Wireless Networks
Ming Xiang, Nicolò Michelusi, Yonina C. Eldar +1
Over-the-air computation has emerged as a scalable and efficient solution for deploying federated learning algorithms in wireless networks by exploiting waveform superposition for…
Resilience Beyond Stationary Client Unavailability: Unlocking Efficient and Unbiased Federated Learning
Ming Xiang, Stratis Ioannidis, Edmund Yeh +2
Due to resource constraints or external and internal uncertainties, clients in real-world federated learning systems are often intermittently available edge devices. In highly dyna…
Efficient Federated Learning against Heterogeneous and Non-stationary Client Unavailability
Ming Xiang, Stratis Ioannidis, Edmund Yeh +2
Addressing intermittent client availability is critical for the real-world deployment of federated learning algorithms. Most prior work either overlooks the potential non-stationar…
Empowering Federated Learning with Implicit Gossiping: Mitigating Connection Unreliability Amidst Unknown and Arbitrary Dynamics
Ming Xiang, Stratis Ioannidis, Edmund Yeh +2
Federated learning is a popular distributed learning approach for training a machine learning model without disclosing raw data. It consists of a parameter server and a possibly la…
Towards Bias Correction of FedAvg over Nonuniform and Time-Varying Communications
Ming Xiang, Stratis Ioannidis, Edmund Yeh +2
Federated learning (FL) is a decentralized learning framework wherein a parameter server (PS) and a collection of clients collaboratively train a model via minimizing a global obje…
Distributed Non-Convex Optimization with One-Bit Compressors on Heterogeneous Data: Efficient and Resilient Algorithms
Ming Xiang, Lili Su
Federated Learning (FL) is a nascent decentralized learning framework under which a massive collection of heterogeneous clients collaboratively train a model without revealing thei…