Peer-to-Peer Non-Bayesian Learning in Finite Time with a Finite Amount of Communication
Author:
Affiliation:
1. Purdue University,Elmore Family School of Electrical and Computer Engineering
2. University of Pennsylvania,Department of Electrical and Systems Engineering
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9992315/9992317/09993184.pdf?arnumber=9993184
Reference17 articles.
1. A New Approach to Distributed Hypothesis Testing and Non-Bayesian Learning: Improved Learning Rate and Byzantine Resilience
2. Defending Non-Bayesian learning against adversarial attacks;su;Distributed Computing,2016
3. Communication-efficient distributed cooperative learning with compressed beliefs;toghani,2021
4. Distributed Inference With Sparse and Quantized Communication
5. Large deviation analysis for learning rate in distributed hypothesis testing
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