12 research outputs found
MODES: model-based optimization on distributed embedded systems
The predictive performance of a machine learning model highly depends on the corresponding hyper-parameter setting. Hence, hyper-parameter tuning is often indispensable. Normally such tuning requires the dedicated machine learning model to be trained and evaluated on centralized data to obtain a performance estimate. However, in a distributed machine learning scenario, it is not always possible to collect all the data from all nodes due to privacy concerns or storage limitations. Moreover, if data has to be transferred through low bandwidth connections it reduces the time available for tuning. Model-Based Optimization (MBO) is one state-of-the-art method for tuning hyper-parameters but the application on distributed machine learning models or federated learning lacks research. This work proposes a framework MODES that allows to deploy MBO on resource-constrained distributed embedded systems. Each node trains an individual model based on its local data. The goal is to optimize the combined prediction accuracy. The presented framework offers two optimization modes: (1) MODES-B considers the whole ensemble as a single black box and optimizes the hyper-parameters of each individual model jointly, and (2) MODES-I considers all models as clones of the same black box which allows it to efficiently parallelize the optimization in a distributed setting. We evaluate MODES by conducting experiments on the optimization for the hyper-parameters of a random forest and a multi-layer perceptron. The experimental results demonstrate that, with an improvement in terms of mean accuracy (MODES-B), run-time efficiency (MODES-I), and statistical stability for both modes, MODES outperforms the baseline, i.e., carry out tuning with MBO on each node individually with its local sub-data set
Bi-fidelity Evolutionary Multiobjective Search for Adversarially Robust Deep Neural Architectures
Deep neural networks have been found vulnerable to adversarial attacks, thus
raising potentially concerns in security-sensitive contexts. To address this
problem, recent research has investigated the adversarial robustness of deep
neural networks from the architectural point of view. However, searching for
architectures of deep neural networks is computationally expensive,
particularly when coupled with adversarial training process. To meet the above
challenge, this paper proposes a bi-fidelity multiobjective neural architecture
search approach. First, we formulate the NAS problem for enhancing adversarial
robustness of deep neural networks into a multiobjective optimization problem.
Specifically, in addition to a low-fidelity performance predictor as the first
objective, we leverage an auxiliary-objective -- the value of which is the
output of a surrogate model trained with high-fidelity evaluations. Secondly,
we reduce the computational cost by combining three performance estimation
methods, i.e., parameter sharing, low-fidelity evaluation, and surrogate-based
predictor. The effectiveness of the proposed approach is confirmed by extensive
experiments conducted on CIFAR-10, CIFAR-100 and SVHN datasets
EvoFed: Leveraging Evolutionary Strategies for Communication-Efficient Federated Learning
Federated Learning (FL) is a decentralized machine learning paradigm that
enables collaborative model training across dispersed nodes without having to
force individual nodes to share data. However, its broad adoption is hindered
by the high communication costs of transmitting a large number of model
parameters. This paper presents EvoFed, a novel approach that integrates
Evolutionary Strategies (ES) with FL to address these challenges. EvoFed
employs a concept of 'fitness-based information sharing', deviating
significantly from the conventional model-based FL. Rather than exchanging the
actual updated model parameters, each node transmits a distance-based
similarity measure between the locally updated model and each member of the
noise-perturbed model population. Each node, as well as the server, generates
an identical population set of perturbed models in a completely synchronized
fashion using the same random seeds. With properly chosen noise variance and
population size, perturbed models can be combined to closely reflect the actual
model updated using the local dataset, allowing the transmitted similarity
measures (or fitness values) to carry nearly the complete information about the
model parameters. As the population size is typically much smaller than the
number of model parameters, the savings in communication load is large. The
server aggregates these fitness values and is able to update the global model.
This global fitness vector is then disseminated back to the nodes, each of
which applies the same update to be synchronized to the global model. Our
analysis shows that EvoFed converges, and our experimental results validate
that at the cost of increased local processing loads, EvoFed achieves
performance comparable to FedAvg while reducing overall communication
requirements drastically in various practical settings
A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends
Computer vision (CV) is a big and important field
in artificial intelligence covering a wide range of applications.
Image analysis is a major task in CV aiming to extract, analyse
and understand the visual content of images. However, imagerelated
tasks are very challenging due to many factors, e.g., high
variations across images, high dimensionality, domain expertise
requirement, and image distortions. Evolutionary computation
(EC) approaches have been widely used for image analysis with
significant achievement. However, there is no comprehensive
survey of existing EC approaches to image analysis. To fill
this gap, this paper provides a comprehensive survey covering
all essential EC approaches to important image analysis tasks
including edge detection, image segmentation, image feature
analysis, image classification, object detection, and others. This
survey aims to provide a better understanding of evolutionary
computer vision (ECV) by discussing the contributions of different
approaches and exploring how and why EC is used for
CV and image analysis. The applications, challenges, issues, and
trends associated to this research field are also discussed and
summarised to provide further guidelines and opportunities for
future research