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Boosting Privately: Privacy-Preserving Federated Extreme Boosting for Mobile Crowdsensing [article]

Yang Liu, Zhuo Ma, Ximeng Liu, Siqi Ma, Surya Nepal, Robert Deng
2020 arXiv   pre-print
Inspired by the two challenges, we propose FedXGB, a federated extreme gradient boosting (XGBoost) scheme supporting forced aggregation. FedXGB mainly achieves the following two breakthroughs.  ...  For the naive HE scheme, the server is set to master the secret key.  ...  CONCLUSION In this paper, we proposed a privacy-preserving federated extreme gradient boosting scheme (FEDXGB) for mobile crowdsensing.  ... 
arXiv:1907.10218v2 fatcat:4alhvnuffvdkzkyzc4ivzuzad4

Cloud-based Federated Boosting for Mobile Crowdsensing [article]

Zhuzhu Wang, Yilong Yang, Yang Liu, Ximeng Liu, Brij B. Gupta, Jianfeng Ma
2020 arXiv   pre-print
In this paper, we propose a secret sharing based federated learning architecture FedXGB to achieve the privacy-preserving extreme gradient boosting for mobile crowdsensing.  ...  The application of federated extreme gradient boosting to mobile crowdsensing apps brings several benefits, in particular high performance on efficiency and classification.  ...  CONCLUSION In this paper, we proposed a privacy-preserving federated learning architecture (FedXGB) for the training of extreme gradient boosting model (XGBoost) in crowdsensing applications.  ... 
arXiv:2005.05304v1 fatcat:vt6wzcpqffbl3lgn4e6o4mp3h4

Federated Learning in Smart City Sensing: Challenges and Opportunities

Ji Chu Jiang, Burak Kantarci, Sema Oktug, Tolga Soyata
2020 Sensors  
A comprehensive discussion of the state-of-the-art methods for Federated Learning is provided along with an in-depth discussion on the applicability of Federated Learning in smart city sensing; clear insights  ...  The advent of the Internet of Things (IoT) and the widespread use of mobile devices with computing and sensing capabilities has motivated applications that require data acquisition at a societal scale.  ...  Horizontal Federated Learning is the strongest candidate for wide adoption in smart cities crowdsensing due to the nature of selective user selection for specific sensing data in Mobile Crowdsensing.  ... 
doi:10.3390/s20216230 pmid:33142863 pmcid:PMC7662977 fatcat:gl7qvweau5gzjmld6n6jabxcee

A Fair and Efficient Hybrid Federated Learning Framework based on XGBoost for Distributed Power Prediction [article]

Haizhou Liu, Xuan Zhang, Xinwei Shen, Hongbin Sun
2022 arXiv   pre-print
To this end, we propose a hybrid federated learning framework based on XGBoost, for distributed power prediction from real-time external features.  ...  To fully exploit the underlying patterns of these distributed data for accurate power prediction, federated learning is needed as a collaborative but privacy-preserving training scheme.  ...  Tian et al. proposed a horizontal and serverless FederBoost framework for private federated learning of gradient boosting decision trees [27] , in which all the uploaded gradients were transferred to  ... 
arXiv:2201.02783v1 fatcat:3xbm4hjh2nhlhahegfwawtrrtm

Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis

Mohamed Amine Ferrag, Othmane Friha, Leandros Maglaras, Helge Janicke, Lei Shu
2021 IEEE Access  
[56] 2019 Privacy-enhanced federated learning scheme Liu et al. [52] 2020 Federated extreme gradient boosting (XG-Boost) scheme Liu et al.  ...  ciphertext of private gradients.  ... 
doi:10.1109/access.2021.3118642 fatcat:222fgsvt3nh6zcgm5qt4kxe7c4

Federated Learning for Internet of Things: A Comprehensive Survey [article]

Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana, Aruna Seneviratne, Jun Li, H. Vincent Poor
2021 arXiv   pre-print
Particularly, we explore and analyze the potential of FL for enabling a wide range of IoT services, including IoT data sharing, data offloading and caching, attack detection, localization, mobile crowdsensing  ...  Federated Learning (FL) has emerged as a distributed collaborative AI approach that can enable many intelligent IoT applications, by allowing for AI training at distributed IoT devices without the need  ...  FL for IoT Mobile Crowdsensing [94] Mobile crowdsens- ing HFL Regression tree Mobile devices Data server A secure FL model for mobile crowdsensing.  ... 
arXiv:2104.07914v1 fatcat:b5wsrfcbynel7jqdxpfw4ftwh4

Federated Learning for Internet of Things: A Comprehensive Survey

Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana, Aruna Seneviratne, Jun Li, H. Vincent Poor
2021 IEEE Communications Surveys and Tutorials  
Particularly, we explore and analyze the potential of FL for enabling a wide range of IoT services, including IoT data sharing, data offloading and caching, attack detection, localization, mobile crowdsensing  ...  Federated Learning (FL) has emerged as a distributed collaborative AI approach that can enable many intelligent IoT applications, by allowing for AI training at distributed IoT devices without the need  ...  FL for IoT Mobile Crowdsensing With the development of IoT, mobile crowdsensing is designed to take advantage of pervasive mobile devices for sensing and collecting data from physical environments to serve  ... 
doi:10.1109/comst.2021.3075439 fatcat:ycq2zydqrzhibfqyo4vzloeoqy

A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection [article]

Qinbin Li, Zeyi Wen, Zhaomin Wu, Sixu Hu, Naibo Wang, Yuan Li, Xu Liu, Bingsheng He
2021 arXiv   pre-print
Similar to deep learning systems such as PyTorch and TensorFlow that boost the development of deep learning, federated learning systems (FLSs) are equivalently important, and face challenges from various  ...  Moreover, we provide a thorough categorization for federated learning systems according to six different aspects, including data distribution, machine learning model, privacy mechanism, communication architecture  ...  [122] propose a federated extreme boosting learning framework for mobile crowdsensing. They adopted secret sharing to achieve privacy-preserving learning of GBDTs. Liu et al.  ... 
arXiv:1907.09693v6 fatcat:d3l2l664mjdfrjgyok43pfxnvq

Privacy-Preserving Federated Learning Framework with General Aggregation and Multiparty Entity Matching

Zhou Zhou, Youliang Tian, Changgen Peng, Zhipeng Cai
2021 Wireless Communications and Mobile Computing  
The requirement for data sharing and privacy has brought increasing attention to federated learning.  ...  Moreover, protocols for multiparty entity matching are rarely covered. Thus, there is no systematic framework to perform federated learning tasks.  ...  [24] propose a secret sharing-based federated extreme boosting learning framework to achieve privacypreserving model training for mobile crowdsensing. Xu et al.  ... 
doi:10.1155/2021/6692061 fatcat:lepqf3spw5ekzjl3coqpq4meo4

2021 Index IEEE Transactions on Knowledge and Data Engineering Vol. 33

2022 IEEE Transactions on Knowledge and Data Engineering  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  ., +, TKDE June 2021 2669-2679 Optimizing Multi-Query Evaluation in Federated RDF Systems. Peng, P., +, TKDE April 2021 1692-1707 Quality Inference Based Task Assignment in Mobile Crowdsensing.  ...  ., +, TKDE May 2021 2121-2136 Nonparametric Regression via Variance-Adjusted Gradient Boosting Gaussian Process Regression.  ... 
doi:10.1109/tkde.2021.3128365 fatcat:4m5kefreyrbhpb3lhzvgqzm3qu

Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directions [article]

Thippa Reddy Gadekallu, Quoc-Viet Pham, Thien Huynh-The, Sweta Bhattacharya, Praveen Kumar Reddy Maddikunta, Madhusanka Liyanage
2021 arXiv   pre-print
To overcome this challenge, federated learning (FL) appeared to be a promising learning technique.  ...  for big data.  ...  Acknowledgement We acknowledge the authors (Dinh, Fang, Pubudu) for the contribution of our (blockchain -big data) development.  ... 
arXiv:2110.04160v2 fatcat:3y2kmamdbrfmrjdxv3zh47yphu

Federated Learning in Mobile Edge Networks: A Comprehensive Survey [article]

Wei Yang Bryan Lim, Nguyen Cong Luong, Dinh Thai Hoang, Yutao Jiao, Ying-Chang Liang, Qiang Yang, Dusit Niyato, Chunyan Miao
2020 arXiv   pre-print
FL can serve as an enabling technology in mobile edge networks since it enables the collaborative training of an ML model and also enables DL for mobile edge network optimization.  ...  However, conventional enabling technologies for ML at mobile edge networks still require personal data to be shared with external parties, e.g., edge servers.  ...  To deal with the GAN attack, the authors in [166] introduce a solution using secret sharing scheme with extreme boosting algorithm.  ... 
arXiv:1909.11875v2 fatcat:a2yxlq672needkejenu4j3izyu

The Future Internet convergence of IMS and ubiquitous smart environments: An IMS-based solution for energy efficiency

Paolo Bellavista, Giuseppe Cardone, Antonio Corradi, Luca Foschini
2012 Journal of Network and Computer Applications  
The push from academia and industry for this kind of services shows that time is mature for a more general support framework for Pervasive Sensing solutions able to enhance frail architectures, promote  ...  The capabilities necessary for Pervasive Sensing are nowadays available on a plethora of devices, from embedded devices to PCs and smartphones.  ...  private spaces.  ... 
doi:10.1016/j.jnca.2011.05.003 fatcat:3iprgfdvmjfxxey67qtflilnri

FMore: An Incentive Scheme of Multi-dimensional Auction for Federated Learning in MEC [article]

Rongfei Zeng, Shixun Zhang, Jiaqi Wang, Xiaowen Chu
2020 arXiv   pre-print
Promising federated learning coupled with Mobile Edge Computing (MEC) is considered as one of the most promising solutions to the AI-driven service provision.  ...  These features require the incentive mechanism to be well designed for MEC. In this paper, we present an incentive mechanism FMore with multi-dimensional procurement auction of K winners.  ...  MEC might also boost the widespread use of federated learning.  ... 
arXiv:2002.09699v1 fatcat:ykiaauv5kzh2pgcrz4exza5b6m

2021 Index IEEE Internet of Things Journal Vol. 8

2021 IEEE Internet of Things Journal  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  ., +, JIoT April 1, 2021 6089-6100 Biobjective Robust Incentive Mechanism Design for Mobile Crowdsensing.  ...  2021 750-765 Incentivizing Differentially Private Federated Learning: A Multidimensional Contract Approach.  ... 
doi:10.1109/jiot.2022.3141840 fatcat:42a2qzt4jnbwxihxp6rzosha3y
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