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Edge Learning for 6G-enabled Internet of Things: A Comprehensive Survey of Vulnerabilities, Datasets, and Defenses – Arxiv

Edge learning is a new and powerful approach to training models across distributed clients while protecting the privacy of their data.
This approach is expected to be embedded within future network infrastructures, including 6G, to solve challenging problems such as resource management and behaviour prediction. However, edge learning in general, and distributed deep learning, in particular, have been discovered to be susceptible to tampering and manipulation. This survey article provides a holistic review of the most recent research focused on edge learning vulnerabilities and defenses for 6G-enabled IoT.

Not Lost in Fog – An Edgeless Global Wireless Network (AKA 6G?)

The concept of an endless fog is not much different from that of 6G, insofar as every device acts both as a transmitter and a receiver, talking to every other device.

If we left it to existing industry players to work this out amongst themselves, we might wait until 99G.
However, the next iteration of the loX (including “things” such as metaverse) is the wild card that may reshape this.

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