A federated learning based approach for loan defaults prediction

Shingi, Geet

doi:10.1109/icdmw51313.2020.00057

Cited by 34 publications

(16 citation statements)

References 15 publications

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“…Alternatively, the federated learning paradigm could be followed, where the data is processed only at edge nodes and that information is then streamed in order to train a global model while preserving the privacy of those nodes [Li et al, 2020]. Federated learning has been successfully applied in many areas, such as healthcare [Xu et al, 2021], sensing in smart cities [Jiang et al, 2020], and financial services [Shingi, 2020, Long et al, 2020. However, there are core challenges of federated learning [Li et al, 2020].…”

Section: Ethical Considerationsmentioning

confidence: 99%

Climate Change & Computer Audition: A Call to Action and Overview on Audio Intelligence to Help Save the Planet

Schuller¹,

Akman²,

Chen³

et al. 2022

Preprint

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Among the seventeen Sustainable Development Goals (SDGs) proposed within the 2030 Agenda and adopted by all the United Nations member states, the 13 th SDG is a call for action to combat climate change for a better world. In this work, we provide an overview of areas in which audio intelligence -a powerful but in this context so far hardly considered technology -can contribute to overcome climate-related challenges. We categorise potential computer audition applications according to the five elements of earth, water, air, fire, and aether, proposed by the ancient Greeks in their five element theory; this categorisation serves as a framework to discuss computer audition in relation to different ecological aspects. Earth and water are concerned with the early detection of environmental changes and, thus, with the protection of humans and animals, as well as the monitoring of land and aquatic organisms. Aerial audio is used to monitor and obtain information about bird and insect populations. Furthermore, acoustic measures can deliver relevant information for the monitoring and forecasting of weather and other meteorological phenomena. The fourth considered element is fire. Due to the burning of fossil fuels, the resulting increase in CO 2 emissions and the associated rise in temperature, fire is used as a symbol for man-made climate change and in this context includes the monitoring of noise pollution, machines, as well as the early detection of wildfires. In all these areas, computer audition can help counteract climate change. Aether then corresponds to the technology itself that makes this possible. This work explores these areas and discusses potential applications, while positioning computer audition in relation to methodological alternatives.

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Section: Ethical Considerationsmentioning

confidence: 99%

Climate Change & Computer Audition: A Call to Action and Overview on Audio Intelligence to Help Save the Planet

Schuller¹,

Akman²,

Chen³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The authors of [156][157][158] presented how federated learning has been used in finance. The authors of [156] presented the management of non-IID data and personalized modelling in the open bank.…”

Section: Applications and Functionalities Of Federated Learningmentioning

confidence: 99%

Federated Learning Design and FunctionalModels: Survey

John

Utomo

Rouniyar

et al. 2022

Preprint

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Federated learning is a multiple device collaboration setup designed tosolve machine learning problems under framework for aggregation andknowledge transfer in distributed local data. This distributed modelensures the privacy of data at each local node. Owing to its relevance,there has been extensive research activities and outcomes in federatedlearning with expanded applicability to different areas by the researchcommunity. As such, there is a vast research archive made available by thecommunity with research work and articles related to the various aspectsof federated learning such as applications, challenges, privacy, function-alities, and design. With respect to the function and design of federatedlearning, client selection, aggregation, knowledge transfer, managementof distributed data (Non-IID), Incentive of data and communication costare of paramount importance. Any effective design of federated learningrequires these aspects to be well considered.There are numerous surveyarticles found among the available literature that focus on its applica-tion and challenges, opportunities, data privacy and protection, as wellas on federated learning on internet of things, federated learning on edgecomputing, etc.1 In this paper, a review of the available literature on the various ele-ments of design and functionalities in federated learning has been carriedout with an aim to lay emphasis on the important challenges andresearch opportunities. More specifically, this work has endeavored tounderstand and summarize the various functional methods available,along with their techniques and goals. Additionally, it has strived toget a bird’s eye view of how various functions and designs of feder-ated learning have been used in applications, and how it has helpeduncover challenges and promising research directions for the future.

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“…Federated learning has received much attention from the research community recently. Numerous FL approaches have been introduced for different applications such as finance [36], healthcare [37], and medical image [38]. To train an FL method, the cross-silo approach has become popular since it enables the training process to use computing resources more effectively [18].…”

Section: Related Workmentioning

confidence: 99%

Deep Federated Learning for Autonomous Driving

Nguyen¹,

Do²,

Tran³

et al. 2021

Preprint

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Autonomous driving is an active research topic in both academia and industry. However, most of the existing solutions focus on improving the accuracy by training learnable models with centralized large-scale data. Therefore, these methods do not take into account the user's privacy. In this paper, we present a new approach to learn autonomous driving policy while respecting privacy concerns. We propose a peer-to-peer Deep Federated Learning (DFL) approach to train deep architectures in a fully decentralized manner and remove the need for central orchestration. We design a new Federated Autonomous Driving network (FADNet) that can improve the model stability, ensure convergence, and handle imbalanced data distribution problems while is being trained with federated learning methods. Intensively experimental results on three datasets show that our approach with FADNet and DFL achieves superior accuracy compared with other recent methods. Furthermore, our approach can maintain privacy by not collecting user data to a central server. Our source code and trained models are available at https://github.com/aioz-ai/FADNet

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A federated learning based approach for loan defaults prediction

Cited by 34 publications

References 15 publications

Climate Change & Computer Audition: A Call to Action and Overview on Audio Intelligence to Help Save the Planet

Climate Change & Computer Audition: A Call to Action and Overview on Audio Intelligence to Help Save the Planet

Federated Learning Design and FunctionalModels: Survey

Deep Federated Learning for Autonomous Driving

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