Securely Aggregated Coded Matrix Inversion

Charalambides, Neophytos; Pilanci, Mert; Hero, Alfred O.

doi:10.1109/jsait.2023.3312233

Securely Aggregated Coded Matrix Inversion

Journal Article · 07 September 2023 · IEEE Journal on Selected Areas in Information Theory

DOI:https://doi.org/10.1109/jsait.2023.3312233· OSTI ID:2283180

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University of Michigan, Ann Arbor, MI (United States); University of Michigan
Stanford University, CA (United States)
University of Michigan, Ann Arbor, MI (United States)

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. Here, the network we consider is not centrally administrated, and the communications which take place are secure against potential eavesdroppers.

View Accepted Manuscript (DOE)

Research Organization:: Georgia Institute of Technology, Atlanta, GA (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: NA0003921

OSTI ID:: 2283180

Journal Information:: IEEE Journal on Selected Areas in Information Theory, Journal Name: IEEE Journal on Selected Areas in Information Theory Vol. 4; ISSN 2641-8770

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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Securely Aggregated Coded Matrix Inversion

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