TDML -- A Trustworthy Distributed Machine Learning Framework ...

Recent years have witnessed a surge in deep learning research, marked by the introduction of expansive generative models like OpenAI's SORA and GPT, Meta AI's LLAMA series, and Google's FLAN, BART, and Gemini models. However, the rapid advancement of large models (LM) has intensified...

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Bibliographic Details
Main Authors: Wang, Zhen, Wang, Qin, Yu, Guangsheng, Chen, Shiping
Format: Report
Language:unknown
Published: arXiv 2024
Subjects:
Cryptography and Security cs.CR
FOS Computer and information sciences
DML
Online Access:https://dx.doi.org/10.48550/arxiv.2407.07339
https://arxiv.org/abs/2407.07339
id ftdatacite:10.48550/arxiv.2407.07339
record_format openpolar
spelling ftdatacite:10.48550/arxiv.2407.07339 2024-09-15T18:03:48+00:00 TDML -- A Trustworthy Distributed Machine Learning Framework ... Wang, Zhen Wang, Qin Yu, Guangsheng Chen, Shiping 2024 https://dx.doi.org/10.48550/arxiv.2407.07339 https://arxiv.org/abs/2407.07339 unknown arXiv Creative Commons Attribution Non Commercial Share Alike 4.0 International https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode cc-by-nc-sa-4.0 Cryptography and Security cs.CR FOS Computer and information sciences article Article Preprint CreativeWork 2024 ftdatacite https://doi.org/10.48550/arxiv.2407.07339 2024-08-01T10:10:52Z Recent years have witnessed a surge in deep learning research, marked by the introduction of expansive generative models like OpenAI's SORA and GPT, Meta AI's LLAMA series, and Google's FLAN, BART, and Gemini models. However, the rapid advancement of large models (LM) has intensified the demand for computing resources, particularly GPUs, which are crucial for their parallel processing capabilities. This demand is exacerbated by limited GPU availability due to supply chain delays and monopolistic acquisition by major tech firms. Distributed Machine Learning (DML) methods, such as Federated Learning (FL), mitigate these challenges by partitioning data and models across multiple servers, though implementing optimizations like tensor and pipeline parallelism remains complex. Blockchain technology emerges as a promising solution, ensuring data integrity, scalability, and trust in distributed computing environments, but still lacks guidance on building practical DML systems. In this paper, we propose a ... Report DML DataCite
institution Open Polar
collection DataCite
op_collection_id ftdatacite
language unknown
topic Cryptography and Security cs.CR
FOS Computer and information sciences
spellingShingle Cryptography and Security cs.CR
FOS Computer and information sciences
Wang, Zhen
Wang, Qin
Yu, Guangsheng
Chen, Shiping
TDML -- A Trustworthy Distributed Machine Learning Framework ...
topic_facet Cryptography and Security cs.CR
FOS Computer and information sciences
description Recent years have witnessed a surge in deep learning research, marked by the introduction of expansive generative models like OpenAI's SORA and GPT, Meta AI's LLAMA series, and Google's FLAN, BART, and Gemini models. However, the rapid advancement of large models (LM) has intensified the demand for computing resources, particularly GPUs, which are crucial for their parallel processing capabilities. This demand is exacerbated by limited GPU availability due to supply chain delays and monopolistic acquisition by major tech firms. Distributed Machine Learning (DML) methods, such as Federated Learning (FL), mitigate these challenges by partitioning data and models across multiple servers, though implementing optimizations like tensor and pipeline parallelism remains complex. Blockchain technology emerges as a promising solution, ensuring data integrity, scalability, and trust in distributed computing environments, but still lacks guidance on building practical DML systems. In this paper, we propose a ...
format Report
author Wang, Zhen
Wang, Qin
Yu, Guangsheng
Chen, Shiping
author_facet Wang, Zhen
Wang, Qin
Yu, Guangsheng
Chen, Shiping
author_sort Wang, Zhen
title TDML -- A Trustworthy Distributed Machine Learning Framework ...
title_short TDML -- A Trustworthy Distributed Machine Learning Framework ...
title_full TDML -- A Trustworthy Distributed Machine Learning Framework ...
title_fullStr TDML -- A Trustworthy Distributed Machine Learning Framework ...
title_full_unstemmed TDML -- A Trustworthy Distributed Machine Learning Framework ...
title_sort tdml -- a trustworthy distributed machine learning framework ...
publisher arXiv
publishDate 2024
url https://dx.doi.org/10.48550/arxiv.2407.07339
https://arxiv.org/abs/2407.07339
genre DML
genre_facet DML
op_rights Creative Commons Attribution Non Commercial Share Alike 4.0 International
https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode
cc-by-nc-sa-4.0
op_doi https://doi.org/10.48550/arxiv.2407.07339
_version_ 1810441268203880448

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