Distributed Gram-Schmidt orthogonalization with simultaneous elements refinement

Slučiak, Ondrej; Straková, Hana; Rupp, Markus; Gansterer, Wilfried N.

doi:10.1186/s13634-016-0322-6

Cited by 5 publications

(2 citation statements)

References 30 publications

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“…However, those algorithms are often highly specialized to the compute system and rely on shared disk storage. For distributed sensor networks, Gram-Schmidt procedures relying on push-sum have been proposed (Sluciak et al, 2016;Straková et al, 2012). However, these methods require peer-to-peer communication and are hence unsuitable for the intended star-like architecture.…”

Section: Federated Matrix Orthonormalizationmentioning

confidence: 99%

Federated singular value decomposition for high dimensional data

Hartebrodt¹,

Röttger²,

Blumenthal³

2022

Preprint

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Federated learning (FL) is emerging as a privacy-aware alternative to classical cloud-based machine learning. In FL, the sensitive data remains in data silos and only aggregated parameters are exchanged. Hospitals and research institutions which are not willing to share their data can join a federated study without breaching confidentiality. In addition to the extreme sensitivity of biomedical data, the high dimensionality poses a challenge in the context of federated genome-wide association studies (GWAS). In this article, we present a federated singular value decomposition (SVD) algorithm, suitable for the privacy-related and computational requirements of GWAS. Notably, the algorithm has a transmission cost independent of the number of samples and is only weakly dependent on the number of features, because the singular vectors associated with the samples are never exchanged and the vectors associated with the features only for a fixed number of iterations. Although motivated by GWAS, the algorithm is generically applicable for both horizontally and vertically partitioned data.

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Section: Federated Matrix Orthonormalizationmentioning

confidence: 99%

Federated singular value decomposition for high dimensional data

Hartebrodt¹,

Röttger²,

Blumenthal³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Federated QR algorithms have been suggested mainly in the field of peer-to-peer networks relying on the PushSum algorithm and gossiping [12], [13], [14]. While these schemes can be implemented in a modern federated learning system, the assumptions governing FL make these algorithms unsuited.…”

Section: Related Workmentioning

confidence: 99%

Privacy of federated QR decomposition using additive secure multiparty computation

Hartebrodt¹,

Röttger²

2022

Preprint

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Federated learning (FL) is a privacy-aware data mining strategy keeping the private data on the owners' machine and thereby confidential. The clients compute local models and send them to an aggregator which computes a global model. In hybrid FL, the local parameters are additionally masked using secure aggregation, such that only the global aggregated statistics become available in clear text, not the client specific updates. Federated QR decomposition has not been studied extensively in the context of cross-silo federated learning. In this article, we investigate the suitability of three QR decomposition algorithms for cross-silo FL and suggest a privacy-aware QR decomposition scheme based on the Gram-Schmidt algorithm which does not blatantly leak raw data. We apply the algorithm to compute linear regression in a federated manner.

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Federated singular value decomposition for high-dimensional data

Hartebrodt,

Röttger,

Blumenthal

2023

Data Min Knowl Disc

View full text Add to dashboard Cite

Federated learning (FL) is emerging as a privacy-aware alternative to classical cloud-based machine learning. In FL, the sensitive data remains in data silos and only aggregated parameters are exchanged. Hospitals and research institutions which are not willing to share their data can join a federated study without breaching confidentiality. In addition to the extreme sensitivity of biomedical data, the high dimensionality poses a challenge in the context of federated genome-wide association studies (GWAS). In this article, we present a federated singular value decomposition algorithm, suitable for the privacy-related and computational requirements of GWAS. Notably, the algorithm has a transmission cost independent of the number of samples and is only weakly dependent on the number of features, because the singular vectors corresponding to the samples are never exchanged and the vectors associated with the features are only transmitted to an aggregator for a fixed number of iterations. Although motivated by GWAS, the algorithm is generically applicable for both horizontally and vertically partitioned data.

show abstract

Distributed Gram-Schmidt orthogonalization with simultaneous elements refinement

Cited by 5 publications

References 30 publications

Federated singular value decomposition for high dimensional data

Federated singular value decomposition for high dimensional data

Privacy of federated QR decomposition using additive secure multiparty computation

Federated singular value decomposition for high-dimensional data

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