Securely Aggregated Coded Matrix Inversion

Coded computing is a method for mitigating straggling workers in a centralized computing network, by using erasure-coding techniques. Federated learning is a decentralized model for training data distributed across client devices. In this work we propose approximating the inverse of an aggregated da...

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Vydané v:IEEE journal on selected areas in information theory Ročník 4; s. 405 - 419
Hlavní autori: Charalambides, Neophytos, Pilanci, Mert, Hero, Alfred O.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Clients
Coding
Coding theory
Computation
Cryptography
Distributed databases
Federated learning
Information theory
Iterative methods
lagrange interpolation
Lagrangian functions
MATHEMATICS AND COMPUTING
Numerical analysis
Optimization
Security
ISSN:2641-8770, 2641-8770
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Shrnutí:Coded computing is a method for mitigating straggling workers in a centralized computing network, by using erasure-coding techniques. Federated learning is a decentralized model for training data distributed across client devices. In this work we propose approximating the inverse of an aggregated data matrix, where the data is generated by clients; similar to the federated learning paradigm, while also being resilient to stragglers. To do so, we propose a coded computing method based on gradient coding. We modify this method so that the coordinator does not access the local data at any point; while the clients access the aggregated matrix in order to complete their tasks. The network we consider is not centrally administrated, and the communications which take place are secure against potential eavesdroppers.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
NA0003921
USDOE
ISSN:2641-8770
2641-8770
DOI:10.1109/JSAIT.2023.3312233