AI RESEARCH
Deep Randomized Distributed Function Computation (DeepRDFC): Neural Distributed Channel Simulation
arXiv CS.AI
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ArXi:2603.10750v1 Announce Type: cross The randomized distributed function computation (RDFC) framework, which unifies many cutting-edge distributed computation and learning applications, is considered. An autoencoder (AE) architecture is proposed to minimize the total variation distance between the probability distribution simulated by the AE outputs and an unknown target distribution, using only data samples. We illustrate significantly high RDFC performance with communication load gains from our AEs compared to data compression methods.