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Proactive Congestion Avoidance for Distributed Deep Learning [PDF]
Sensors, 2020 This paper presents “Proactive Congestion Notification” (PCN), a congestion-avoidance technique for distributed deep learning (DDL). DDL is widely used to scale out and accelerate deep neural network training.
Minkoo Kang +3 more
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Towards accelerating model parallelism in distributed deep learning systems. [PDF]
PLoS ONE, 2023 Modern deep neural networks cannot be often trained on a single GPU due to large model size and large data size. Model parallelism splits a model for multiple GPUs, but making it scalable and seamless is challenging due to different information sharing ...
Hyeonseong Choi +3 more
doaj +4 more sources
Quantum distributed deep learning architectures: Models, discussions, and applications
ICT Express, 2023 Although deep learning (DL) has already become a state-of-the-art technology for various data processing tasks, data security and computational overload problems often arise due to their high data and computational power dependency. To solve this problem,
Yunseok Kwak +7 more
doaj +3 more sources
Distributed Deep Learning in IoT Sensor Network for the Diagnosis of Plant Diseases [PDF]
SensorsThe early detection of plant diseases is critical to improving agricultural productivity and ensuring food security. However, conventional centralized deep learning approaches are often unsuitable for large-scale agricultural deployments, as they rely on
Athanasios Papanikolaou +4 more
doaj +2 more sources
Coin.AI: A Proof-of-Useful-Work Scheme for Blockchain-Based Distributed Deep Learning [PDF]
Entropy, 2019 One decade ago, Bitcoin was introduced, becoming the first cryptocurrency and establishing the concept of “blockchain” as a distributed ledger.
Alejandro Baldominos, Yago Saez
doaj +2 more sources
Private and Secure Distributed Deep Learning: A Survey
ACM Computing SurveysTraditionally, deep learning practitioners would bring data into a central repository for model training and inference. Recent developments in distributed learning, such as federated learning and deep learning as a service (DLaaS), do not require centralized data and instead push computing to where the distributed datasets reside.
Henri E Bal +2 more
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A snapshot of parallelism in distributed deep learning training
Revista Colombiana de ComputaciónThe accelerated development of applications related to artificial intelligence has generated the creation of increasingly complex neural network models with enormous amounts of parameters, currently reaching up to trillions of parameters.
Hairol Romero-Sandí +2 more
doaj +4 more sources
Learned Gradient Compression for Distributed Deep Learning [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2022 15 pages 24 ...
Lusine Abrahamyan +3 more
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Distributed deep learning networks among institutions for medical imaging. [PDF]
J Am Med Inform Assoc, 2018 Chang K +8 more
europepmc +2 more sources
Distributed Deep Learning in Open Collaborations
CoRR, 2021 Modern deep learning applications require increasingly more compute to train state-of-the-art models. To address this demand, large corporations and institutions use dedicated High-Performance Computing clusters, whose construction and maintenance are both environmentally costly and well beyond the budget of most organizations.
Michael Diskin +15 more
openaire +3 more sources