Novel Constructions for Computation and Communication Trade-offs in Private Coded Distributed Computing

Distributed computing enables scalable machine learning by distributing tasks across multiple nodes, but ensuring privacy in such systems remains a challenge. This paper introduces a novel private coded distributed computing model that integrates privacy constraints to keep task assignments hidden....

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Veröffentlicht in:IEEE transactions on communications S. 1
Hauptverfasser: Sasi, Shanuja, Gunlu, Onur
Format: Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 2025
Schlagworte:
Coded distributed computing
Computational modeling
Distributed computing
Electronic mail
Encoding
Feature extraction
Indexes
Load modeling
Machine learning algorithms
MapReduce framework
placement delivery arrays
Privacy
private computing
Servers
ISSN:0090-6778, 1558-0857
Online-Zugang:Volltext
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Zusammenfassung:Distributed computing enables scalable machine learning by distributing tasks across multiple nodes, but ensuring privacy in such systems remains a challenge. This paper introduces a novel private coded distributed computing model that integrates privacy constraints to keep task assignments hidden. By leveraging placement delivery arrays (PDAs), we design an extended PDA framework to characterize achievable computation and communication loads under privacy constraints. By constructing two classes of extended PDAs, we explore the trade-offs between computation and communication, showing that although privacy increases communication overhead, it can be significantly alleviated through optimized PDA-based coded strategies.
ISSN:0090-6778
1558-0857
DOI:10.1109/TCOMM.2025.3631533