Privacy-Preserving Coded Mobile Edge Computing for Low-Latency Distributed Inference

@article{Schlegel2021PrivacyPreservingCM,
  title={Privacy-Preserving Coded Mobile Edge Computing for Low-Latency Distributed Inference},
  author={Reent Schlegel and Siddhartha Kumar and Eirik Rosnes and Alexandre Graell i Amat},
  journal={IEEE Journal on Selected Areas in Communications},
  year={2021},
  volume={40},
  pages={788-799}
}
We consider a mobile edge computing scenario where a number of devices want to perform a linear inference <inline-formula> <tex-math notation="LaTeX">${W}{x} $ </tex-math></inline-formula> on some local data <inline-formula> <tex-math notation="LaTeX">$ {x}$ </tex-math></inline-formula> given a network-side matrix <inline-formula> <tex-math notation="LaTeX">$ {W}$ </tex-math></inline-formula>. The computation is performed at the network edge over a number of edge servers. We propose a coding… 
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