296 citations · 554 across the 18 of their papers we have counts for
4 papers · 1 filter
Federated Learning for Inference at Anytime and Anywhere
Zicheng Liu, Da Li, Javier Fernandez-Marques +6
Federated learning has been predominantly concerned with collaborative training of deep networks from scratch, and especially the many challenges that arise, such as communication…
The Future of Consumer Edge-AI Computing
Stefanos Laskaridis, Stylianos I. Venieris, Alexandros Kouris +2
In the last decade, Deep Learning has rapidly infiltrated the consumer end, mainly thanks to hardware acceleration across devices. However, as we look towards the future, it is evi…
Fluid Batching: Exit-Aware Preemptive Serving of Early-Exit Neural Networks on Edge NPUs
Alexandros Kouris, Stylianos I. Venieris, Stefanos Laskaridis +1
With deep neural networks (DNNs) emerging as the backbone in a multitude of computer vision tasks, their adoption in real-world applications broadens continuously. Given the abunda…
FedorAS: Federated Architecture Search under system heterogeneity
Lukasz Dudziak, Stefanos Laskaridis, Javier Fernandez-Marques
Federated learning (FL) has recently gained considerable attention due to its ability to learn on decentralised data while preserving client privacy. However, it also poses additio…