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| 2. |
- Balador, Ali, et al.
(författare)
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DAIS Project - Distributed Artificial Intelligence Systems : Objectives and Challenges
- 2023
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Ingår i: ACM SIGAda Ada Letters. - : Association for Computing Machinery. - 1094-3641 .- 1557-9476. ; 42:2, s. 96-98
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Tidskriftsartikel (refereegranskat)abstract
- DAIS is a step forward in the area of artificial intelligence and edge computing. DAIS intends to create a complete framework for self-organizing, energy efficient and private-by-design distributed AI. DAIS is a European project with a consortium of 47 partners from 11 countries coordinated by RISE Research Institute of Sweden.
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| 3. |
- Geraeinejad, V., et al.
(författare)
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RoCo-NAS : Robust and Compact Neural Architecture Search
- 2021
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Ingår i: Proceedings of the International Joint Conference on Neural Networks. - : Institute of Electrical and Electronics Engineers Inc.. ; July
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Tidskriftsartikel (refereegranskat)abstract
- Deep model compression has been studied widely, and state-of-the-art methods can now achieve high compression ratios with minimum accuracy loss. Recent advances in adversarial attacks reveal the inherent vulnerability of deep neural networks to slightly perturbed images called adversarial examples. Since then, extensive efforts have been performed to enhance deep networks’ robustness via specialized loss functions and learning algorithms. Previous works suggest that network size and robustness against adversarial examples contradict on most occasions. In this paper, we investigate how to optimize compactness and robustness to adversarial attacks of neural network architectures while maintaining the accuracy using multi-objective neural architecture search. We propose the use of previously generated adversarial examples as an objective to evaluate the robustness of our models in addition to the number of floating-point operations to assess model complexity i.e. compactness. Experiments on some recent neural architecture search algorithms show that due to their limited search space they fail to find robust and compact architectures. By creating a novel neural architecture search (RoCo-NAS), we were able to evolve an architecture that is up to 7% more accurate against adversarial samples than its more complex architecture counterpart. Thus, the results show inherently robust architectures regardless of their size. This opens up a new range of possibilities for the exploration and design of deep neural networks using automatic architecture search.
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| 4. |
- Loni, Mohammad, et al.
(författare)
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DeepMaker : A multi-objective optimization framework for deep neural networks in embedded systems
- 2020
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Ingår i: Microprocessors and microsystems. - : Elsevier B.V.. - 0141-9331 .- 1872-9436. ; 73
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Tidskriftsartikel (refereegranskat)abstract
- Deep Neural Networks (DNNs) are compute-intensive learning models with growing applicability in a wide range of domains. Due to their computational complexity, DNNs benefit from implementations that utilize custom hardware accelerators to meet performance and response time as well as classification accuracy constraints. In this paper, we propose DeepMaker framework that aims to automatically design a set of highly robust DNN architectures for embedded devices as the closest processing unit to the sensors. DeepMaker explores and prunes the design space to find improved neural architectures. Our proposed framework takes advantage of a multi-objective evolutionary approach that exploits a pruned design space inspired by a dense architecture. DeepMaker considers the accuracy along with the network size factor as two objectives to build a highly optimized network fitting with limited computational resource budgets while delivers an acceptable accuracy level. In comparison with the best result on the CIFAR-10 dataset, a generated network by DeepMaker presents up to a 26.4x compression rate while loses only 4% accuracy. Besides, DeepMaker maps the generated CNN on the programmable commodity devices, including ARM Processor, High-Performance CPU, GPU, and FPGA.
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| 5. |
- Loni, Mohammad, et al.
(författare)
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NeuroPower : Designing Energy Efficient Convolutional Neural Network Architecture for Embedded Systems
- 2019
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Ingår i: Lecture Notes in Computer Science, Volume 11727. - Munich, Germany : Springer. - 9783030304867 ; 11727 LNCS, s. 208-222
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Konferensbidrag (refereegranskat)abstract
- Convolutional Neural Networks (CNNs) suffer from energy-hungry implementation due to their computation and memory intensive processing patterns. This problem is even more significant by the proliferation of CNNs on embedded platforms. To overcome this problem, we offer NeuroPower as an automatic framework that designs a highly optimized and energy efficient set of CNN architectures for embedded systems. NeuroPower explores and prunes the design space to find improved set of neural architectures. Toward this aim, a multi-objective optimization strategy is integrated to solve Neural Architecture Search (NAS) problem by near-optimal tuning network hyperparameters. The main objectives of the optimization algorithm are network accuracy and number of parameters in the network. The evaluation results show the effectiveness of NeuroPower on energy consumption, compacting rate and inference time compared to other cutting-edge approaches. In comparison with the best results on CIFAR-10/CIFAR-100 datasets, a generated network by NeuroPower presents up to 2.1x/1.56x compression rate, 1.59x/3.46x speedup and 1.52x/1.82x power saving while loses 2.4%/-0.6% accuracy, respectively.
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| 6. |
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| 7. |
- Mishchenko, Kateryna, et al.
(författare)
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Hyperparameters Optimization for Federated Learning System : Speech Emotion Recognition Case Study
- 2023
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Ingår i: 2023 Eighth International Conference on Fog and Mobile Edge Computing (FMEC). - : IEEE. ; , s. 80-86
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Konferensbidrag (refereegranskat)abstract
- Context: Federated Learning (FL) has emerged as a promising, massively distributed way to train a joint deep model across numerous edge devices, ensuring user data privacy by retaining it on the device. In FL, Hyperparameters (HP) significantly affect the training overhead regarding computation and transmission time, computation and transmission load, as well as model accuracy. This paper presents a novel approach where Hyperparameters Optimization (HPO) is used to optimize the performance of the FL model for Speech Emotion Recognition (SER) application. To solve this problem, both Single-Objective Optimization (SOO) and Multi-Objective Optimization (MOO) models are developed and evaluated. The optimization model includes two objectives: accuracy and total execution time. Numerical results show that optimal Hyperparameters (HP) settings allow for improving both the accuracy of the model and its computation time. The proposed method assists FL system designers in finding optimal parameters setup, allowing them to carry out model design and development efficiently depending on their goals.
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| 8. |
- Mohammadi, Mohammadreza, et al.
(författare)
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Anomaly Detection Using LSTM-Autoencoder in Smart Grid : A Federated Learning Approach
- 2023
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Ingår i: <em>ACM International Conference Proceeding Series</em>. - : Association for Computing Machinery. ; , s. 48-54
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Konferensbidrag (refereegranskat)abstract
- ABSTRACT. Anomaly detection is critical in industrial systems such as smart grid systems to guarantee their safe and effective operation. The smart grid stations contain sensitive data, and they are concerned about sharing it with a third-party server to establish a centralized anomaly detection system. Federated Learning (FL) is a feasible solution to these problems for enhancing anomaly detection in smart grid systems. This study describes a method for developing an unsupervised anomaly detection based on FL system using a synthetic dataset based on real-world grid system behavior. The paper investigates the usage of FL’s long short-term memory autoencoder (LSTM-AE) for anomaly detection. For more accurate identification, this research explores the performance of integrating LSTM-AE with one-class support vector machine (OC-SVM) and isolation forest (IF) and compares their results with a threshold-based anomaly detection approach. Moreover, an approach is described for generating synthetic anomalies with different levels of difficulty to evaluate the robustness of the anomaly detection FL model. The FL models results are compared with the centralized version of the models as a baseline and the results show that FL models outperformed the centralized approach by detecting higher outlier data by achieving 99% F1-Score.
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| 9. |
- Mohammadi, Mohammadreza, et al.
(författare)
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Privacy-preserving Federated Learning System for Fatigue Detection
- 2023
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Ingår i: Proceedings of the 2023 IEEE International Conference on Cyber Security and Resilience, CSR 2023. - : Institute of Electrical and Electronics Engineers Inc.. ; , s. 624-629
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Tidskriftsartikel (refereegranskat)abstract
- Context:. Drowsiness affects the driver’s cognitive abilities, which are all important for safe driving. Fatigue detection is a critical technique to avoid traffic accidents. Data sharing among vehicles can be used to optimize fatigue detection models and ensure driving safety. However, data privacy issues hinder the sharing process. To tackle these challenges, we propose a Federated Learning (FL) approach for fatigue-driving behavior monitoring. However, in the FL system, the privacy information of the drivers might be leaked. In this paper, we propose to combine the concept of differential privacy (DP) with Federated Learning for the fatigue detection application, in which artificial noise is added to parameters at the drivers’ side before aggregating. This approach will ensure the privacy of drivers’ data and the convergence of the federated learning algorithms. In this paper, the privacy level in the system is determined in order to achieve a balance between the noise scale and the model’s accuracy. In addition, we have evaluated our models resistance against a model inversion attack. The effectiveness of the attack is measured by the Mean Squared Error (MSE) between the reconstructed data point and the training data. The proposed approach, compared to the non-DP case, has a 6% accuracy loss while decreasing the effectiveness of the attacks by increasing the MSE from 5.0 to 7.0, so a balance between accuracy and noise scale is achieved.
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| 10. |
- Mohammadi, Samaneh, et al.
(författare)
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Balancing Privacy and Accuracy in Federated Learning for Speech Emotion Recognition
- 2023
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Ingår i: ACSIS Annals of Computer Science and Information Systems. - : Institute of Electrical and Electronics Engineers (IEEE). ; 35, s. 191-199, s. 191-200
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Tidskriftsartikel (refereegranskat)abstract
- Context: Speech Emotion Recognition (SER) is a valuable technology that identifies human emotions from spoken language, enabling the development of context-aware and personalized intelligent systems. To protect user privacy, Federated Learning (FL) has been introduced, enabling local training of models on user devices. However, FL raises concerns about the potential exposure of sensitive information from local model parameters, which is especially critical in applications like SER that involve personal voice data. Local Differential Privacy (LDP) has prevented privacy leaks in image and video data. However, it encounters notable accuracy degradation when applied to speech data, especially in the presence of high noise levels. In this paper, we propose an approach called LDP-FL with CSS, which combines LDP with a novel client selection strategy (CSS). By leveraging CSS, we aim to improve the representatives of updates and mitigate the adverse effects of noise on SER accuracy while ensuring client privacy through LDP. Furthermore, we conducted model inversion attacks to evaluate the robustness of LDP-FL in preserving privacy. These attacks involved an adversary attempting to reconstruct individuals' voice samples using the output labels provided by the SER model. The evaluation results reveal that LDP-FL with CSS achieved an accuracy of 65-70%, which is 4% lower than the initial SER model accuracy. Furthermore, LDP-FL demonstrated exceptional resilience against model inversion attacks, outperforming the non-LDP method by a factor of 10. Overall, our analysis emphasizes the importance of achieving a balance between privacy and accuracy in accordance with the requirements of the SER application.
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