1 citations · 1 across the 9 of their papers we have counts for
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GP-FL: Model-Based Hessian Estimation for Second-Order Over-the-Air Federated Learning
Shayan Mohajer Hamidi, Ali Bereyhi, Saba Asaad +1
Second-order methods are widely adopted to improve the convergence rate of learning algorithms. In federated learning (FL), these methods require the clients to share their local H…
A Rate-Distortion Analysis for Composite Sources Under Subsource-Dependent Fidelity Criteria
Jiakun Liu, H. Vincent Poor, Iickho Song +1
A composite source, consisting of multiple subsources and a memoryless switch, outputs one symbol at a time from the subsource selected by the switch. If some data should be encode…
Rethinking Resource Management in Edge Learning: A Joint Pre-training and Fine-tuning Design Paradigm
Zhonghao Lyu, Yuchen Li, Guangxu Zhu +3
In some applications, edge learning is experiencing a shift in focusing from conventional learning from scratch to new two-stage learning unifying pre-training and task-specific fi…
Adaptive Federated Learning Over the Air
Chenhao Wang, Zihan Chen, Nikolaos Pappas +3
We propose a federated version of adaptive gradient methods, particularly AdaGrad and Adam, within the framework of over-the-air model training. This approach capitalizes on the in…