Large Deviations for Products of Non-Identically Distributed Network Matrices With Applications to Communication-Efficient Distributed Learning and Inference

Author:

Petrović Nemanja1ORCID,Bajović Dragana2,Kar Soummya3ORCID,Jakovetić Dušan4ORCID,Sahu Anit Kumar5ORCID

Affiliation:

1. Department of Fundamental Sciences, Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia

2. Department of Power, Electronics, and Communications Engineering, Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia

3. Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA

4. Department of Mathematics and Informatics, Faculty of Sciences, University of Novi Sad, Novi Sad, Serbia

5. Amazon.com, Inc., Alexa AI, Pittsburgh, PA, USA

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Signal Processing

Reference51 articles.

1. LAG: Lazily aggregated gradient for communication-efficient distributed learning;chen;Proc 32nd Int Conf Neural Inf Process Syst,0

2. Communication-efficient distributed optimization with quantized preconditioners;alimisis;Proc Int Conf Mach Learn,0

3. Distributed learning for cooperative inference;nedi?,2017

4. Communication-efficient federated learning via robust distributed mean estimation;vargaftik;Mach Learn Res,0

5. Communication-Efficient Distributed Cooperative Learning With Compressed Beliefs

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