AutoML with Bayesian Optimizations for Big Data Management

Karras, Aristeidis; Karras, Christos; Schizas, Nikolaos; Avlonitis, Markos; Sioutas, Spyros

doi:10.3390/info14040223

Cited by 12 publications

(4 citation statements)

References 47 publications

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“…Hidden Markov models (HMM) [11] have been used to model the underlying probabilistic structure of data in Bayesian clustering. Accelerating hyperparameters via Bayesian optimizations can also help in building automated machine learning (AutoML) schemes [12], while such optimizations can also be applied in Tiny Machine Learning (TinyML) environments wherein devices can be trained to fulfil ML tasks [13]. Ensemble Bayesian Clustering [14] is a variation of Bayesian clustering that combines multiple models to produce more robust results, while cluster analysis [15] extends traditional clustering methods by considering the uncertainty in the data, which leads to more accurate results.…”

Section: Related Workmentioning

confidence: 99%

Consensus Big Data Clustering for Bayesian Mixture Models

Karras

Giotopoulos

et al. 2023

Algorithms

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In the context of big-data analysis, the clustering technique holds significant importance for the effective categorization and organization of extensive datasets. However, pinpointing the ideal number of clusters and handling high-dimensional data can be challenging. To tackle these issues, several strategies have been suggested, such as a consensus clustering ensemble that yields more significant outcomes compared to individual models. Another valuable technique for cluster analysis is Bayesian mixture modelling, which is known for its adaptability in determining cluster numbers. Traditional inference methods such as Markov chain Monte Carlo may be computationally demanding and limit the exploration of the posterior distribution. In this work, we introduce an innovative approach that combines consensus clustering and Bayesian mixture models to improve big-data management and simplify the process of identifying the optimal number of clusters in diverse real-world scenarios. By addressing the aforementioned hurdles and boosting accuracy and efficiency, our method considerably enhances cluster analysis. This fusion of techniques offers a powerful tool for managing and examining large and intricate datasets, with possible applications across various industries.

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Section: Related Workmentioning

confidence: 99%

Consensus Big Data Clustering for Bayesian Mixture Models

Karras

Giotopoulos

et al. 2023

Algorithms

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“…The prospect of combining Federated Learning (FL) and Automated Machine Learning (AutoML) opens new opportunities for enhancing data privacy and optimising Big Data management. The integration of FL with AutoML, as envisioned in the studies [88,89], provides a robust foundation for the development of the Federated Adversarial Attack for Multi-Task Learning (FAAMT) algorithm. This algorithm aims to address the complexities of multi-task learning within a federated framework, where the goal is to enable the collaborative training of models across multiple tasks while ensuring data privacy and robustness against adversarial attacks.…”

Section: Future Workmentioning

confidence: 99%

FLIBD: A Federated Learning-Based IoT Big Data Management Approach for Privacy-Preserving over Apache Spark with FATE

Karras,

Giannaros,

Theodorakopoulos

et al. 2023

Electronics

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In this study, we introduce FLIBD, a novel strategy for managing Internet of Things (IoT) Big Data, intricately designed to ensure privacy preservation across extensive system networks. By utilising Federated Learning (FL), Apache Spark, and Federated AI Technology Enabler (FATE), we skilfully investigated the complicated area of IoT data management while simultaneously reinforcing privacy across broad network configurations. Our FLIBD architecture was thoughtfully designed to safeguard data and model privacy through a synergistic integration of distributed model training and secure model consolidation. Notably, we delved into an in-depth examination of adversarial activities within federated learning contexts. The Federated Adversarial Attack for Multi-Task Learning (FAAMT) was thoroughly assessed, unmasking its proficiency in showcasing and exploiting vulnerabilities across various federated learning approaches. Moreover, we offer an incisive evaluation of numerous federated learning defence mechanisms, including Romoa and RFA, in the scope of the FAAMT. Utilising well-defined evaluation metrics and analytical processes, our study demonstrated a resilient framework suitable for managing IoT Big Data across widespread deployments, while concurrently presenting a solid contribution to the progression and discussion surrounding defensive methodologies within the federated learning and IoT areas.

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“…More advanced approaches, such as Bayesian optimization, leverage probabilistic models to search the hyperparameter space intelligently [21]. Furthermore, algorithmic advancements like automatic hyperparameter tuning methods, utilizing meta-learning or reinforcement learning itself, have been developed to automate the selection of hyperparameters, enhancing the performance of the algorithms [22]. Difficulties in handling continuous and high-dimensional spaces are overcome using deep neural networks and parameterization methods.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning-based Wi-Fi Contention Window Optimization

Cruz,

Ouameur,

Figueiredo

2023

JCIS

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The collision avoidance mechanism adopted by the IEEE 802.11 standard is not optimal. The mechanism employs a binary exponential backoff (BEB) algorithm in the medium access control (MAC) layer. Such an algorithm increases the backoff interval whenever a collision is detected to minimize the probability of subsequent collisions. However, the increase of the backoff interval causes degradation of the radio spectrum utilization (i.e., bandwidth wastage). That problem worsens when the network has to manage the channel access to a dense number of stations, leading to a dramatic decrease in network performance. Furthermore, a wrong backoff setting increases the probability of collisions such that the stations experience numerous collisions before achieving the optimal backoff value. Therefore, to mitigate bandwidth wastage and, consequently, maximize the network performance, this work proposes using reinforcement learning (RL) algorithms, namely Deep Q Learning (DQN) and Deep Deterministic Policy Gradient (DDPG), to tackle such an optimization problem. In our proposed approach, we assess two different observation metrics, the average of the normalized level of the transmission queue of all associated stations and the probability of collisions. The overall network’s throughput is defined as the reward. The action is the contention window (CW) value that maximizes throughput while minimizing the number of collisions. As for the simulations, the NS-3 network simulator is used along with a toolkit known as NS3-gym, which integrates a reinforcement-learning (RL) framework into NS-3. The results demonstrate that DQN and DDPG have much better performance than BEB for both static and dynamic scenarios, regardless of the number of stations. Additionally, our results show that observations based on the average of the normalized level of the transmission queues have a slightly better performance than observations based on the collision probability. Moreover, the performance difference with BEB is amplified as the number of stations increases, with DQN and DDPG showing a 45.52% increase in throughput with 50 stations.

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AutoML with Bayesian Optimizations for Big Data Management

Cited by 12 publications

References 47 publications

Consensus Big Data Clustering for Bayesian Mixture Models

Consensus Big Data Clustering for Bayesian Mixture Models

FLIBD: A Federated Learning-Based IoT Big Data Management Approach for Privacy-Preserving over Apache Spark with FATE

Reinforcement Learning-based Wi-Fi Contention Window Optimization

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