2 citations · 3 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…
Fair Concurrent Training of Multiple Models in Federated Learning
Marie Siew, Haoran Zhang, Jong-Ik Park +6
Federated learning (FL) enables collaborative learning across multiple clients. In most FL work, all clients train a single learning task. However, the recent proliferation of FL a…
Distributed Experimental Design Networks
Yuanyuan Li, Lili Su, Carlee Joe-Wong +2
As edge computing capabilities increase, model learning deployments in diverse edge environments have emerged. In experimental design networks, introduced recently, network routing…