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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 |