Federated Learning for Computationally-Constrained Heterogeneous Devices: A Survey

With an increasing number of smart devices like internet of things (IoT) devices deployed in the field, offloadingtraining of neural networks (NNs) to a central server becomes more and more infeasible. Recent efforts toimprove users' privacy have led to on-device learning emerging as an alterna...

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Veröffentlicht in:	arXiv.org 2023-07
Hauptverfasser:	Pfeiffer, Kilian, Rapp, Martin, Khalili, Ramin, Henkel, Jörg
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Learning Distillation Federated learning Heterogeneity Internet of Things Model accuracy Neural networks Privacy
Online-Zugang:	Volltext
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