Which Design Decisions in AI-enabled Mobile Applications Contribute to Greener AI?

Creus, Castanyer, Roger; Martínez‐Fernández, Silverio; Franch, Xavier

doi:10.48550/arxiv.2109.15284

2021

DOI: 10.48550/arxiv.2109.15284

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Which Design Decisions in AI-enabled Mobile Applications Contribute to Greener AI?

Castanyer, Roger Creus¹,

Silverio Martínez‐Fernández²,

Xavier Franch³

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2023

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Cited by 2 publications

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References 33 publications

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“…Information granules and the area of intelligent computing revolving around them being termed granular computing are quite often presented with a direct association with the pioneering studies by Zadeh [3] . He coined an informal, yet highly descriptive, and compelling concept of information granules.…”

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confidence: 99%

Advancing Federated Learning with Granular Computing

Pedrycz¹

2023

Fuzzy Information and Engineering

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Over the recent years, we have been witnessing spectacular achievements of Artificial Intelligence (AI) and Machine Learning (ML), in particular. We have seen highly visible accomplishments encountered in natural language processing and computer vision impacting numerous areas of human endeavours. Being driven inherently by the technologically advanced learning and architectural developments, ML constructs are highly impactful coming with far reaching consequences; just to mention autonomous vehicles, health care imaging, decision-making processes in critical areas, among others. The quality of ML architectures and credibility of generated results are inherently implied by the nature, quality, and amount of available data. The credibility of ML models and confidence quantified their results are also of paramount concern to any critical application. In this study, we advocate that the credibility (confidence) of results produced by ML constructs is inherently expressed in the form of information granules. Several development scenarios are carefully revisited including those involving constructs in statistics (confidence and prediction intervals), probability (Gaussian process models), and granular parameters (fuzzy sets and interval techniques). We augment the commonly encountered and challenging category of applications of ML referred to as federated learning where the aspect of quality of the model and its results calls for a thorough assessment.

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Advancing Federated Learning with Granular Computing

Pedrycz¹

2023

Fuzzy Information and Engineering

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Guiding the retraining of convolutional neural networks against adversarial inputs

Durán

Martínez‐Fernández

Felderer

et al. 2023

PeerJ Computer Science

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Background When using deep learning models, one of the most critical vulnerabilities is their exposure to adversarial inputs, which can cause wrong decisions (e.g., incorrect classification of an image) with minor perturbations. To address this vulnerability, it becomes necessary to retrain the affected model against adversarial inputs as part of the software testing process. In order to make this process energy efficient, data scientists need support on which are the best guidance metrics for reducing the adversarial inputs to create and use during testing, as well as optimal dataset configurations. Aim We examined six guidance metrics for retraining deep learning models, specifically with convolutional neural network architecture, and three retraining configurations. Our goal is to improve the convolutional neural networks against the attack of adversarial inputs with regard to the accuracy, resource utilization and execution time from the point of view of a data scientist in the context of image classification. Method We conducted an empirical study using five datasets for image classification. We explore: (a) the accuracy, resource utilization, and execution time of retraining convolutional neural networks with the guidance of six different guidance metrics (neuron coverage, likelihood-based surprise adequacy, distance-based surprise adequacy, DeepGini, softmax entropy and random), (b) the accuracy and resource utilization of retraining convolutional neural networks with three different configurations (one-step adversarial retraining, adversarial retraining and adversarial fine-tuning). Results We reveal that adversarial retraining from original model weights, and by ordering with uncertainty metrics, gives the best model w.r.t. accuracy, resource utilization, and execution time. Conclusions Although more studies are necessary, we recommend data scientists use the above configuration and metrics to deal with the vulnerability to adversarial inputs of deep learning models, as they can improve their models against adversarial inputs without using many inputs and without creating numerous adversarial inputs. We also show that dataset size has an important impact on the results.

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Which Design Decisions in AI-enabled Mobile Applications Contribute to Greener AI?

Cited by 2 publications

References 33 publications

Advancing Federated Learning with Granular Computing

Advancing Federated Learning with Granular Computing

Guiding the retraining of convolutional neural networks against adversarial inputs

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