2,880 research outputs found
AdaComp : Adaptive Residual Gradient Compression for Data-Parallel Distributed Training
Highly distributed training of Deep Neural Networks (DNNs) on future compute
platforms (offering 100 of TeraOps/s of computational capacity) is expected to
be severely communication constrained. To overcome this limitation, new
gradient compression techniques are needed that are computationally friendly,
applicable to a wide variety of layers seen in Deep Neural Networks and
adaptable to variations in network architectures as well as their
hyper-parameters. In this paper we introduce a novel technique - the Adaptive
Residual Gradient Compression (AdaComp) scheme. AdaComp is based on localized
selection of gradient residues and automatically tunes the compression rate
depending on local activity. We show excellent results on a wide spectrum of
state of the art Deep Learning models in multiple domains (vision, speech,
language), datasets (MNIST, CIFAR10, ImageNet, BN50, Shakespeare), optimizers
(SGD with momentum, Adam) and network parameters (number of learners,
minibatch-size etc.). Exploiting both sparsity and quantization, we demonstrate
end-to-end compression rates of ~200X for fully-connected and recurrent layers,
and ~40X for convolutional layers, without any noticeable degradation in model
accuracies.Comment: IBM Research AI, 9 pages, 7 figures, AAAI18 accepte
Crowd-ML: A Privacy-Preserving Learning Framework for a Crowd of Smart Devices
Smart devices with built-in sensors, computational capabilities, and network
connectivity have become increasingly pervasive. The crowds of smart devices
offer opportunities to collectively sense and perform computing tasks in an
unprecedented scale. This paper presents Crowd-ML, a privacy-preserving machine
learning framework for a crowd of smart devices, which can solve a wide range
of learning problems for crowdsensing data with differential privacy
guarantees. Crowd-ML endows a crowdsensing system with an ability to learn
classifiers or predictors online from crowdsensing data privately with minimal
computational overheads on devices and servers, suitable for a practical and
large-scale employment of the framework. We analyze the performance and the
scalability of Crowd-ML, and implement the system with off-the-shelf
smartphones as a proof of concept. We demonstrate the advantages of Crowd-ML
with real and simulated experiments under various conditions
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