A Systematic Literature Review on Federated Learning: From A Model Quality Perspective

Liu, Yi; Zhang, Li; Ge, Ning; Li, Guanghao

doi:10.48550/arxiv.2012.01973

Cited by 5 publications

(4 citation statements)

References 45 publications

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“…The concept of FL was originally designed and developed to enable the training of ML algorithms while adhering to privacy regulations [208]. FL attempts to protect the security and privacy of local raw training data by maintaining it at its source or storage location, without ever transferring it to a central server [209].…”

Section: Privacymentioning

confidence: 99%

A Survey on the Use of Synthetic Data for Enhancing Key Aspects of Trustworthy AI in the Energy Domain: Challenges and Opportunities

Meiser,

Zinnikus

2024

Energies

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To achieve the energy transition, energy and energy efficiency are becoming more and more important in society. New methods, such as Artificial Intelligence (AI) and Machine Learning (ML) models, are needed to coordinate supply and demand and address the challenges of the energy transition. AI and ML are already being applied to a growing number of energy infrastructure applications, ranging from energy generation to energy forecasting and human activity recognition services. Given the rapid development of AI and ML, the importance of Trustworthy AI is growing as it takes on increasingly responsible tasks. Particularly in the energy domain, Trustworthy AI plays a decisive role in designing and implementing efficient and reliable solutions. Trustworthy AI can be considered from two perspectives, the Model-Centric AI (MCAI) and the Data-Centric AI (DCAI) approach. We focus on the DCAI approach, which relies on large amounts of data of sufficient quality. These data are becoming more and more synthetically generated. To address this trend, we introduce the concept of Synthetic Data-Centric AI (SDCAI). In this survey, we examine Trustworthy AI within a Synthetic Data-Centric AI context, focusing specifically on the role of simulation and synthetic data in enhancing the level of Trustworthy AI in the energy domain.

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Section: Privacymentioning

confidence: 99%

A Survey on the Use of Synthetic Data for Enhancing Key Aspects of Trustworthy AI in the Energy Domain: Challenges and Opportunities

Meiser,

Zinnikus

2024

Energies

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“…Veri mahremiyeti ve kişisel verilerin güvenliği adına oluşan problemlere çözüm olarak, ilk defa Google 2016 yılında federe öğrenme kavramını ortaya koymuştur [25]. Veri gizliliği temelli oluşturulan bu öğrenme modeli sağlık, eğitim, akıllı şehir, giyilebilir cihazlar, finans, blok zincir ve nesnelerin interneti gibi önemli ve geniş bir alanda uygulamalara sahiptir [26,27].…”

Section: İlgili çAlışmalar (Related Work)unclassified

du-CBA: Veriden habersiz ve artırımlı sınıflandırmaya dayalı birliktelik kuralları çıkarma mimarisi

BÜYÜKTANIR>

Yildiz

Ülkü

et al. 2023

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

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İstemcilerin (uçta çalışan cihazlar) ve sunucuların birlikte çalışması gereken sistemlerde makine öğrenmesi modeli kullanılması bir ihtiyaçtır. Ancak istemcilerden verilerin toplanması, sunucuya aktarılması, makine öğrenmesi modeli eğitilmesi ve ardından bu modelin istemcilerde çalışan cihazlara entegre edilmesi bir çok problemi beraberinde getirmektedir. Verilerin istemcilerden sunucuya transferi ağ trafiğine sebep olmakta ve fazla enerji gerektirmektedir. Bütün veriler aktarılacağı için veri mahremiyeti de istismar edilebilmektedir. Çalışma kapsamında, bu problemlerin çözümü için bir mimari önerilmektedir. Mimariye göre her bir istemcide kendi verilerinden bir makine öğrenmesi modeli eğitilmektedir. Eğitilen model sunucuya gönderilmekte ve sunucuda bu modeller birleştirilerek yeni bir model oluşturulmaktadır. Oluşturulan nihai model tekrar istemcilere dağıtılmaktadır. Bu sayede ağ trafiği azaltılmakta, enerji ihtiyacı düşürülmekte ve veri mahremiyeti bütün veriler yerine tek başına anlamsız olan veriler gönderildiği için korunmaktadır. Ayrıca tüm istemcilerdeki modeller güncel kalmaktadır. Bu çalışma kapsamında önerilen mimariye ait bir simülasyon ortamı oluşturulmuştur. Simülasyon ortamında iki farklı şekilde model eğitimi gerçekleştirilmiş ve elde edilen iki model kıyaslanmıştır. İlk model istemcilerden sunucuya verilerin aktarılmasıyla ve bu verilerden model eğitilmesiyle elde edilmiştir. İkincisi ise önerilen mimari olan istemcilerde model eğitilip sunucuda modellerin birleştirilmesi ile nihai modelin eğitilmesidir. Önerilen mimariyi simülasyonda gerçekleştirmek için Incremental Classification Based on Association (I-CBA) olarak adlandırılan algoritma geliştirilmiştir. I-CBA ve ilişkisel sınıflandırma algoritmalarından biri olan CBA kullanılarak, UCI veri havuzundan alınan beş veri kümesi ile modeller eğitilmiştir. Deneysel sonuçlar CBA ile karşılaştırıldığında, I-CBA ile model eğitim süresinin yaklaşık olarak %70 oranında azaldığını ve neredeyse aynı doğruluğu elde ettiğini göstermiştir. Bu sonuçlar önerilen mimarinin başarıya ulaştığını ortaya koymaktadır.

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“…These papers are not focusing on trust factor. Authors in [27] conduct a systematic literature model quality perspective in FL. The paper examines various aspects of model quality, including model selection, but does not specifically address the other trustworthy aspects.…”

Section: Literature Reviewmentioning

confidence: 99%

Common misconceptions regarding COVID-19 among health care professionals: an online global cross-sectional survey

Tariq¹,

Hamid²,

Mashood³

et al. 2020

J Oral Res

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Background: The COVID-19 pandemic has taken the world by storm, with cases continuously increasing by the hour and with a shortage of information regarding the virus available to the general public. Despite the availability of trusted online sources, there are still misconceptions relating to the virus floating around. This study aimed to find out the level of misconceptions among healthcare professionals (HCPs) worldwide about the ensuing COVID-19 pandemic. Materials and Methods: A descriptive cross sectional study was conducted through a global online survey. The self-administered questionnaire was designed and registered at an online website (Kwiksurveys). A total of 652 participants from 35 different countries across the world responded. Statistical analysis was performed using SPSS version 23. Socio-demographic and discipline characteristics were compared with the help of the chi-square test and univariate and multivariate logistic regression to find significant relationships. Results: Among all HCPs, general physicians (61.2%) were the most common respondents. The responses from females (63.3%) almost doubled those of men, and nearly half of the participants were working in private institutes (49.2%). Additionally, the major source of information used by HCPs about COVID-19 was social media (55.4%). Regarding misconception assessment, 71.6% of participants had correct concepts regarding COVID-19. However, 28.4% had incorrect information. Female HCPs were 1.49 times more likely to have correct concepts compared to males (OR=1.49, 95% CI=1.04-2.14). Conclusions: The majority of the HCPs were keeping themselves up-to-date with current information concerning the knowledge, prevention, and hygiene practices of COVID-19 infection. However, some misconceptions are deeply rooted in the mindsets of HCPs worldwide and need to be addressed by the continuous professional development of HCPs. The availability of reliable sources of information on the pandemic should be encouraged, with adequate explanations also available to the general public in simple terms.

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A Systematic Literature Review on Federated Learning: From A Model Quality Perspective

Cited by 5 publications

References 45 publications

A Survey on the Use of Synthetic Data for Enhancing Key Aspects of Trustworthy AI in the Energy Domain: Challenges and Opportunities

A Survey on the Use of Synthetic Data for Enhancing Key Aspects of Trustworthy AI in the Energy Domain: Challenges and Opportunities

du-CBA: Veriden habersiz ve artırımlı sınıflandırmaya dayalı birliktelik kuralları çıkarma mimarisi

Common misconceptions regarding COVID-19 among health care professionals: an online global cross-sectional survey

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