| 1. |
- Ahmed, Laeeq, et al.
(author)
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Efficient iterative virtual screening with Apache Spark and conformal prediction
- 2018
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In: Journal of Cheminformatics. - : BioMed Central. - 1758-2946. ; 10
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Journal article (peer-reviewed)abstract
- Background: Docking and scoring large libraries of ligands against target proteins forms the basis of structure-based virtual screening. The problem is trivially parallelizable, and calculations are generally carried out on computer clusters or on large workstations in a brute force manner, by docking and scoring all available ligands. Contribution: In this study we propose a strategy that is based on iteratively docking a set of ligands to form a training set, training a ligand-based model on this set, and predicting the remainder of the ligands to exclude those predicted as 'low-scoring' ligands. Then, another set of ligands are docked, the model is retrained and the process is repeated until a certain model efficiency level is reached. Thereafter, the remaining ligands are docked or excluded based on this model. We use SVM and conformal prediction to deliver valid prediction intervals for ranking the predicted ligands, and Apache Spark to parallelize both the docking and the modeling. Results: We show on 4 different targets that conformal prediction based virtual screening (CPVS) is able to reduce the number of docked molecules by 62.61% while retaining an accuracy for the top 30 hits of 94% on average and a speedup of 3.7. The implementation is available as open source via GitHub (https://github.com/laeeq80/spark-cpvs) and can be run on high-performance computers as well as on cloud resources.
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| 2. |
- Ait-Mlouk, Addi, 1990-, et al.
(author)
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FedBot : Enhancing Privacy in Chatbots with Federated Learning
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Other publication (other academic/artistic)abstract
- Chatbots are mainly data-driven and usually based on utterances that might be sensitive. However, training deep learning models on shared data can violate user privacy. Such issues have commonly existed in chatbots since their inception. In the literature, there have been many approaches to deal with privacy, such as differential privacy and secure multi-party computation, but most of them need to have access to users' data. In this context, Federated Learning (FL) aims to protect data privacy through distributed learning methods that keep the data in its location. This paper presents Fedbot, a proof-of-concept (POC) privacy-preserving chatbot that leverages large-scale customer support data. The POC combines Deep Bidirectional Transformer models and federated learning algorithms to protect customer data privacy during collaborative model training. The results of the proof-of-concept showcase the potential for privacy-preserving chatbots to transform the customer support industry by delivering personalized and efficient customer service that meets data privacy regulations and legal requirements. Furthermore, the system is specifically designed to improve its performance and accuracy over time by leveraging its ability to learn from previous interactions.
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| 3. |
- Ait-Mlouk, Addi, et al.
(author)
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FedQAS : Privacy-Aware Machine Reading Comprehension with Federated Learning
- 2022
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In: Applied Sciences. - : MDPI. - 2076-3417. ; 12:6
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Journal article (peer-reviewed)abstract
- Machine reading comprehension (MRC) of text data is a challenging task in Natural Language Processing (NLP), with a lot of ongoing research fueled by the release of the Stanford Question Answering Dataset (SQuAD) and Conversational Question Answering (CoQA). It is considered to be an effort to teach computers how to "understand" a text, and then to be able to answer questions about it using deep learning. However, until now, large-scale training on private text data and knowledge sharing has been missing for this NLP task. Hence, we present FedQAS, a privacy-preserving machine reading system capable of leveraging large-scale private data without the need to pool those datasets in a central location. The proposed approach combines transformer models and federated learning technologies. The system is developed using the FEDn framework and deployed as a proof-of-concept alliance initiative. FedQAS is flexible, language-agnostic, and allows intuitive participation and execution of local model training. In addition, we present the architecture and implementation of the system, as well as provide a reference evaluation based on the SQuAD dataset, to showcase how it overcomes data privacy issues and enables knowledge sharing between alliance members in a Federated learning setting.
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| 4. |
- AL-Naday, Mays, et al.
(author)
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Service-based Federated Deep Reinforcement Learning for Anomaly Detection in Fog Ecosystems
- 2023
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In: 2023 26th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN). - : Institute of Electrical and Electronics Engineers (IEEE). - 9798350398045 - 9798350398052 ; , s. 121-128
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Conference paper (peer-reviewed)abstract
- With Digital transformation, the diversity of services and infrastructure in backhaul fog network(s) is rising to unprecedented levels. This is causing a rising threat of a wider range of cyber attacks coupled with a growing integration of constrained range of infrastructure, particularly seen at the network edge. Deep reinforcement-based learning is an attractive approach to detecting attacks, as it allows less dependency on labeled data with better ability to classify different attacks. However, current approaches to learning are known to be computationally expensive (cost) and the learning experience can be negatively impacted by the presence of outliers and noise (quality). This work tackles both the cost and quality challenges with a novel service-based federated deep reinforcement learning solution, enabling anomaly detection and attack classification at a reduced data cost and with better quality. The federated settings in the proposed approach enable multiple edge units to create clusters that follow a bottom-up learning approach. The proposed solution adapts deep Q-learning Network (DQN) for service-tunable flow classification, and introduces a novel federated DQN (FDQN) for federated learning. Through such targeted training and validation, variation in data patterns and noise is reduced. This leads to improved performance per service with lower training cost. Performance and cost of the solution, along with sensitivity to exploration parameters are evaluated using an example publicly available dataset (UNSW-NB15). Evaluation results show the proposed solution to maintain detection accuracy with lower data supply, while improving the classification rate by a factor of ≈ 2.
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| 5. |
- Alawadi, Sadi, 1983-, et al.
(author)
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Toward efficient resource utilization at edge nodes in federated learning
- 2024
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In: Progress in Artificial Intelligence. - : Springer Science+Business Media B.V.. - 2192-6352 .- 2192-6360.
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Journal article (peer-reviewed)abstract
- Federated learning (FL) enables edge nodes to collaboratively contribute to constructing a global model without sharing their data. This is accomplished by devices computing local, private model updates that are then aggregated by a server. However, computational resource constraints and network communication can become a severe bottleneck for larger model sizes typical for deep learning (DL) applications. Edge nodes tend to have limited hardware resources (RAM, CPU), and the network bandwidth and reliability at the edge is a concern for scaling federated fleet applications. In this paper, we propose and evaluate a FL strategy inspired by transfer learning in order to reduce resource utilization on devices, as well as the load on the server and network in each global training round. For each local model update, we randomly select layers to train, freezing the remaining part of the model. In doing so, we can reduce both server load and communication costs per round by excluding all untrained layer weights from being transferred to the server. The goal of this study is to empirically explore the potential trade-off between resource utilization on devices and global model convergence under the proposed strategy. We implement the approach using the FL framework FEDn. A number of experiments were carried out over different datasets (CIFAR-10, CASA, and IMDB), performing different tasks using different DL model architectures. Our results show that training the model partially can accelerate the training process, efficiently utilizes resources on-device, and reduce the data transmission by around 75% and 53% when we train 25%, and 50% of the model layers, respectively, without harming the resulting global model accuracy. Furthermore, our results demonstrate a negative correlation between the number of participating clients in the training process and the number of layers that need to be trained on each client’s side. As the number of clients increases, there is a decrease in the required number of layers. This observation highlights the potential of the approach, particularly in cross-device use cases. © The Author(s) 2024.
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| 7. |
- Appleton, O, et al.
(author)
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The next-generation ARC middleware
- 2010
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In: ANNALS OF TELECOMMUNICATIONS-ANNALES DES TELECOMMUNICATIONS. - : Presses Polytechniques Romandes. - 0003-4347 .- 1958-9395. ; 65:11-12, s. 771-776
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Journal article (peer-reviewed)abstract
- The Advanced Resource Connector (ARC) is a light-weight, non-intrusive, simple yet powerful Grid middleware capable of connecting highly heterogeneous computing and storage resources. ARC aims at providing general purpose, flexible, collaborative computing environments suitable for a range of uses, both in science and business. The server side offers the fundamental job execution management, information and data capabilities required for a Grid. Users are provided with an easy to install and use client which provides a basic toolbox for job- and data management. The KnowARC project developed the next-generation ARC middleware, implemented as Web Services with the aim of standard-compliant interoperability.
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