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نتیجه جستجو - Federated learning

تعداد مقالات یافته شده: 7
ردیف عنوان نوع
1 Quantum Federated Learning With Decentralized Data
یادگیری فدرال کوانتومی با داده های غیرمتمرکز-2022
Variational quantum algorithm (VQA) accesses the centralized data to train the model, and using distributed computing can significantly improve the training overhead; however, the data is privacy sensitive. In this paper, we propose communication-efficient learning of VQA from decentralized data, which is so-called quantumfederated learning(QFL).Motivated by the classical federated learning algorithm, we improve data privacy by aggregating updates from local computation to share model parameters. Here, aiming to find approximate optima in the parameter landscape, we develop an extension of the conventional VQA. Finally, we deploy onthe TensorFlowQuantum processor within variational quantumtensor networks classifiers, approximate quantum optimization for the Ising model, and variational quantum eigensolver for molecular hydrogen. Our algorithm demonstrates model accuracy from decentralized data, which have higher performance on near-term processors. Importantly, QFL may inspire new investigations in the field of secure quantum machine learning.
Index Terms: Quantum algorithm | quantum computing | quantum information | quantum machine learning.
مقاله انگلیسی
2 Federated learning with hyperparameter-based clustering for electrical load forecasting
یادگیری فدرال با خوشه‌بندی مبتنی بر فراپارامتر برای پیش‌بینی بار الکتریکی-2022
Electrical load prediction has become an integral part of power system operation. Deep learning models have found popularity for this purpose. However, to achieve a desired prediction accuracy, they require huge amounts of data for training. Sharing electricity consumption data of individual households for load prediction may compromise user privacy and can be expensive in terms of communication resources. Therefore, edge computing methods, such as federated learning, are gaining more importance for this purpose. These methods can take advantage of the data without centrally storing it. This paper evaluates the performance of federated learning for short-term forecasting of individual house loads as well as the aggregate load. It discusses the advantages and disadvantages of this method by comparing it to centralized and local learning schemes. Moreover, a new client clustering method is proposed to reduce the convergence time of federated learning. The results show that federated learning has a good performance with a minimum root mean squared error (RMSE) of 0.117 kWh for individual load forecasting.
Keywords: Federated learning | Electricity load forecasting | Edge computing | LSTM | Decentralized learning
مقاله انگلیسی
3 Trustworthy AI in the Age of Pervasive Computing and Big Data
هوش مصنوعی قابل اعتماد در عصر محاسبات فراگیر و داده های بزرگ-2020
The era of pervasive computing has resulted in countless devices that continuously monitor users and their environment, generating an abundance of user behavioural data. Such data may support improving the quality of service, but may also lead to adverse usages such as surveillance and advertisement. In parallel, Artificial Intelligence (AI) systems are being applied to sensitive fields such as healthcare, justice, or human resources, raising multiple concerns on the trustworthiness of such systems. Trust in AI systems is thus intrinsically linked to ethics, including the ethics of algorithms, the ethics of data, or the ethics of practice. In this paper, we formalise the requirements of trustworthy AI systems through an ethics perspective. We specifically focus on the aspects that can be integrated into the design and development of AI systems. After discussing the state of research and the remaining challenges, we show how a concrete use-case in smart cities can benefit from these methods.
Index Terms: Artificial Intelligence | Pervasive Computing | Ethics | Data Fusion | Transparency | Privacy | Fairness | Accountability | Federated Learning
مقاله انگلیسی
4 Knowledge Federation: A Unified and Hierarchical Privacy-Preserving AI Framework
فدراسیون دانش: یک چارچوب متحد و سلسله مراتبی حفظ حریم خصوصی هوش مصنوعی-2020
With strict protections and regulations of data privacy and security, conventional machine learning based on centralized datasets is confronted with significant challenges, making artificial intelligence (AI) impractical in many missioncritical and data-sensitive scenarios, such as finance, government, and health. In the meantime, tremendous datasets are scattered in isolated silos in various industries, organizations, different units of an organization, or different branches of an international organization. These valuable data resources are well underused. To advance AI theories and applications, we propose a comprehensive framework (called Knowledge Federation - KF) to address these challenges by enabling AI while preserving data privacy and ownership. Beyond the concepts of federated learning and secure multi-party computation, KF consists of four levels of federation: (1) information level, low-level statistics and computation of data, meeting the requirements of simple queries, searching and simplistic operators; (2) model level, supporting training, learning, and inference; (3) cognition level, enabling abstract feature representation at various levels of abstractions and contexts; (4) knowledge level, fusing knowledge discovery, representation, and reasoning. We further clarify the relationship and differentiation between knowledge federation and other related research areas. We have developed a reference implementation of KF, called iBond Platform, to offer a productionquality KF platform to enable industrial applications in finance, insurance, marketing, and government. The iBond platform will also help establish the KF community and a comprehensive ecosystem and usher in a novel paradigm shift towards secure, privacy-preserving and responsible AI. As far as we know, knowledge federation is the first hierarchical and unified framework for secure multi-party computing (statistics, queries, searching, and low-level operations) and learning (training, representation, discovery, inference, and reasoning).
Index Terms: Knowledge Federation |Knowledge | Federated Learning | Secure Multi-party Computation | Secure Multi-party Learning
مقاله انگلیسی
5 Defence against the dark artefacts: Smart home cybercrimes and cybersecurity standards
دفاع در برابر مصنوعات تاریک: جرایم سایبری خانه های هوشمند و استانداردهای امنیت سایبری-2020
This paper analyses the assumptions underpinning a range of emerging EU and UK smart home cybersecurity standards. We use internet of things (IoT) case studies (such as the Mirai Botnet affair) and the criminological concept of ‘routine activity theory’ to situate our critique. Our study shows that current cybersecurity standards mainly assume smart home environments are (and will continue to be) underpinned by cloud architectures. This is a short- coming in the longevity of standards. This paper argues that edge computing approaches, such as personal information management systems, are emerging for the IoT and challenge the cloud focused assumptions of these standards. In edge computing, data can be stored in a decentralized manner, locally and analyzed on the client using federated learning. This can have advantages for security, privacy and legal compliance, over centralized cloud-based approaches, particularly around cross border data flows and edge based security analytics. As a consequence, standards should start to reflect the increased interest in this trend to make them more aspirational and responsive for the long term; as ultimately, current IoT architectures are a choice, as opposed to inherent. Our paper unpacks the importance of the adoption of edge computing models which could enable better management of external cyber-criminality threats in smart homes. We also briefly discuss challenges of building smart homes that can accommodate the complex nature of everyday life in the home. In addition to technical aspects, the social and interactional complexities of the home mean internal threats can also emerge. As these human factors remain unresolved in current approaches to smart home cybersecurity, a user’s security can be impacted by such technical design choices.© 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Keyword: Internet of Things | Smart homes | Standards | Security | Cloud | Edge computing
مقاله انگلیسی
6 ‘‘DRL + FL’’: An intelligent resource allocation model based on deep reinforcement learning for Mobile Edge Computing
"DRL + FL": یک مدل تخصیص منابع هوشمند مبتنی بر یادگیری تقویت عمیق برای محاسبات لبه تلفن همراه-2020
With the emergence of a large number of computation-intensive and time-sensitive applications, smart terminal devices with limited resources can only run the model training part of most intelligent applications in the cloud, so a large amount of training data needs to be uploaded to the cloud. This is an important cause of core network communication congestion and poor Quality-of-Experience (QoE) of user. As an important extension and supplement of cloud computing, Mobile Edge Computing (MEC) sinks computing and storage resources from the cloud to the vicinity of User Mobile Devices (UMDs), greatly reducing service latency and alleviating the burden on core networks. However, due to the high cost of edge servers deployment and maintenance, MEC also has the problems of limited network resources and computing resources, and the edge network environment is complex and mutative. Therefore, how to reasonably allocate network resources and computing resources in a changeable MEC environment has become a great aporia. To combat this issue, this paper proposes an intelligent resource allocation model ‘‘DRL + FL’’. Based on this model, an intelligent resource allocation algorithm DDQN-RA based on the emerging DRL algorithm framework DDQN is designed to adaptively allocate network and computing resources. At the same time, the model integrates the FL framework with the mobile edge system to train DRL agents in a distributed way. This model can well solve the problems of uploading large amounts of training data via wireless channels, Non-IID and unbalance of training data when training DRL agents, restrictions on communication conditions, and data privacy. Experimental results show that the proposed ‘‘DRL + FL’’ model is superior to the traditional resource allocation algorithms SDR and LOBO and the intelligent resource allocation algorithm DRLRA in three aspects: minimizing the average energy consumption of the system, minimizing the average service delay, and balancing resource allocation.
Keywords: Mobile edge computing | Intelligent resource allocation | Deep reinforcement learning | Federated learning
مقاله انگلیسی
7 Distributed learning on 20 000+ lung cancer patients – The Personal Health Train
یادگیری توزیع شده بر روی 20 000+ بیمار مبتلا به سرطان ریه - آموزش بهداشت شخصی-2020
Background and purpose: Access to healthcare data is indispensable for scientific progress and innovation. Sharing healthcare data is time-consuming and notoriously difficult due to privacy and regulatory concerns. The Personal Health Train (PHT) provides a privacy-by-design infrastructure connecting FAIR (Findable, Accessible, Interoperable, Reusable) data sources and allows distributed data analysis and machine learning. Patient data never leaves a healthcare institute. Materials and methods: Lung cancer patient-specific databases (tumor staging and post-treatment survival information) of oncology departments were translated according to a FAIR data model and stored locally in a graph database. Software was installed locally to enable deployment of distributed machine learning algorithms via a central server. Algorithms (MATLAB, code and documentation publicly available) are patient privacy-preserving as only summary statistics and regression coefficients are exchanged with the central server. A logistic regression model to predict post-treatment two-year survival was trained and evaluated by receiver operating characteristic curves (ROC), root mean square prediction error (RMSE) and calibration plots. Results: In 4 months, we connected databases with 23 203 patient cases across 8 healthcare institutes in 5 countries (Amsterdam, Cardiff, Maastricht, Manchester, Nijmegen, Rome, Rotterdam, Shanghai) using the PHT. Summary statistics were computed across databases. A distributed logistic regression model predicting post-treatment two-year survival was trained on 14 810 patients treated between 1978 and 2011 and validated on 8 393 patients treated between 2012 and 2015. Conclusion: The PHT infrastructure demonstrably overcomes patient privacy barriers to healthcare data sharing and enables fast data analyses across multiple institutes from different countries with different regulatory regimens. This infrastructure promotes global evidence-based medicine while prioritizing patient privacy.
Keywords: Lung cancer | Big data | Distributed learning | Federated learning | Machine learning | Survival analysis | Prediction modeling | FAIR data
مقاله انگلیسی
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