Deep Randomized Distributed Function Computation (DeepRDFC): Neural Distributed Channel Simulation
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...
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| Veröffentlicht in: | Proceedings / IEEE International Symposium on Information Theory S. 1 - 6 |
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22.06.2025
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| ISSN: | 2157-8117 |
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| Abstract | 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. Our designs establish deep learning-based RDFC methods and aim to facilitate the use of RDFC methods, especially when the amount of common randomness is limited and strong function computation guarantees are required. |
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| AbstractList | 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. Our designs establish deep learning-based RDFC methods and aim to facilitate the use of RDFC methods, especially when the amount of common randomness is limited and strong function computation guarantees are required. |
| Author | Bergstrom, Didrik Gunlu, Onur |
| Author_xml | – sequence: 1 givenname: Didrik surname: Bergstrom fullname: Bergstrom, Didrik email: didrik.bergstrom@liu.se organization: Information Theory and Security Laboratory (ITSL), Linköping University,Sweden – sequence: 2 givenname: Onur surname: Gunlu fullname: Gunlu, Onur email: onur.gunlu@liu.se organization: Information Theory and Security Laboratory (ITSL), Linköping University,Sweden |
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| SubjectTerms | Autoencoders Computer architecture Data compression Design methodology Heating systems Image coding Information theory Network architecture Probability distribution Total variance |
| Title | Deep Randomized Distributed Function Computation (DeepRDFC): Neural Distributed Channel Simulation |
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