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Learning End-to-End Channel Coding with Diffusion Models
Furthermore, we examine the trade-off between sample quality and sampling speed, when an accelerated sampling algorithm is deployed, and investigate the effect of the noise scheduling on this trade-off.
Learning End-to-End Channel Coding with Diffusion Models
This places the challenge to approximate or generate the channel or its derivative from samples generated by pilot signaling in real-world scenarios.
Federated Learning with Local Differential Privacy: Trade-offs between Privacy, Utility, and Communication
We consider Gaussian mechanisms to preserve local differential privacy (LDP) of user data in the FL model with SGD.
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