Feb 15, 2017 · In order to reduce this stress, we propose adaComp, a novel algorithm for compressing worker updates to the model on the server. Applicable to ...
It is shown that distributed deep learning computation on WAN connected devices feasible, in spite of the traffic caused by learning tasks, and that such a ...
We then experiment and measure the impact of compression, device heterogeneity and reliability on the accuracy of learned models, with an emulator platform that ...
... They propose a deep gradient compression (DGC) method to avoid the loss of accuracy, which uses momentum correction and local gradient clipping on top of ...
Distributed deep learning on edge-devices: feasibility via adaptive compression. C Hardy, E Le Merrer, B Sericola. 2017 IEEE 16th International Symposium on ...
Distributed deep learning on edge-devices: Feasibility via adaptive compression. Conference Paper. Oct 2017. Corentin Hardy · Erwan Le Merrer · Bruno Sericola.
The goal of my PhD was to develop the deep learning in distributed systems. The principal motivation is to move learning processes directly on the edge-devices ...
Feb 12, 2024 · Learned Gradient Compression for Distributed Deep Learning ... Distributed deep learning on edge-devices: feasibility via adaptive compression.
Distributed Deep Learning on Edge-Devices: Feasibility Via Adaptive Compression. RESEARCH PAPER / NCA 2017 / Oct 2017 / Machine/Deep Learning/AI, Network ...
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Distributed deep learning on edge-devices: Feasibility via adaptive compression · Computer Science, Engineering. IEEE International Symposium on Network ...