A comparative evaluation of aggregation methods for machine learning over vertically partitioned data

Trevizan, Bernardo; Chamby-Diaz, Jorge Cristhian; Bazzan, Ana L. C.; Recamonde‐Mendoza, Mariana

doi:10.1016/j.eswa.2020.113406

Cited by 14 publications

(8 citation statements)

References 28 publications

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“…The distributed clients only share the training results after the process ends. The history of sharing results can be traced back to ensemble ML over partitioned datasets [227], [228], where a number of base classifiers collectively determine the output for an instance based on a pre-defined aggregation strategy. Ensemble techniques were originally introduced to increase the overall performance of the final classification, but it is also straightforward to utilize it for distributed ML systems [229].…”

Section: Level 3: Sharing Resultsmentioning

confidence: 99%

Trusted AI in Multi-agent Systems: An Overview of Privacy and Security for Distributed Learning

Ma¹,

Li²,

Kang³

et al. 2022

Preprint

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Motivated by the advancing computational capacity of distributed end-user equipments (UEs), as well as the increasing concerns about sharing private data, there has been considerable recent interest in machine learning (ML) and artificial intelligence (AI) that can be processed on on distributed UEs. Specifically, in this paradigm, parts of an ML process are outsourced to multiple distributed UEs, and then the processed ML information is aggregated on a certain level at a central server, which turns a centralized ML process into a distributed one, and brings about significant benefits. However, this new distributed ML paradigm raises new risks of privacy and security issues. In this paper, we provide a survey of the emerging security and privacy risks of distributed ML from a unique perspective of information exchange levels, which are defined according to the key steps of an ML process, i.e.: i) the level of preprocessed data, ii) the level of learning models, iii) the level of extracted knowledge and, iv) the level of intermediate results. We explore and analyze the potential of threats for each information exchange level based on an overview of the current state-of-the-art attack mechanisms, and then discuss the possible defense methods against such threats. Finally, we complete the survey by providing an outlook on the challenges and possible directions for future research in this critical area.

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Section: Level 3: Sharing Resultsmentioning

confidence: 99%

Trusted AI in Multi-agent Systems: An Overview of Privacy and Security for Distributed Learning

Ma¹,

Li²,

Kang³

et al. 2022

Preprint

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“…To date, one of the main directions for improving the quality of processed data is to combine models into various ensembles. Interest in such methods does not fade despite the prevalence of the neural network approach [8,9] because the need arises to implement hybrid models that combine deep learning methods with classical classification algorithms to improve quality indicators in data processing. Such symbiosis for particular tasks makes it possible to significantly improve quality indicators [10,11].…”

Section: -Literature Reviewmentioning

confidence: 99%

Improving the Quality Indicators of Multilevel Data Sampling Processing Models Based on Unsupervised Clustering

Lebedev,

Sukhoparov

2024

Emerg Sci J

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This paper presents a solution for building and implementing data processing models and experimentally evaluates new possibilities for improving ensemble methods based on multilevel data processing models. This study proposes a model to reduce the cost of retraining models when transforming data properties. The research objective is to improve the quality indicators of machine learning models when solving classification problems. The novelty is a method that uses a multilevel architecture of data processing models to determine the current data properties in segments at different levels and assign algorithms with the best quality indicators. This method differs from the known ones by using several model levels that analyze data properties and assign the best models to individual segments of data and training. The improvement consists of using unsupervised clustering of data samples. The resulting clusters are separate subsamples for assigning the best machine-learning models and algorithms. Experimental values of quality indicators for different classifiers on the whole sample and different segments were obtained. The findings show that unsupervised clustering using multilevel models can significantly improve the quality indicators of “weak” classifiers. The quality indicators of individual classifiers improve when the number of data clusters is increased to a certain threshold. The results obtained are applicable to classification when developing models and machine learning methods. The proposed method improved the classification quality indicators by 2–9% due to segmentation and the assignment of models with the best quality indicators in individual segments. Doi: 10.28991/ESJ-2024-08-01-025 Full Text: PDF

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“…When it comes to privacy challenges in FL, research efforts usually focus on statistical inference by combining multiple datasets from different sources. These efforts use methods such as the statistical estimator, risk utility [61], and binary hypothesis testing [62], which are successfully developed in many scenarios with radiation and partitioned data sets [63]. We need models that can configure an appropriate set of attributes or the optimal combination of attributes to identify individuals such as name, address, and telephone number.…”

Section: Comparison With Prior Workmentioning

confidence: 99%

Architectural Design of a Blockchain-Enabled, Federated Learning Platform for Algorithmic Fairness in Predictive Health Care: Design Science Study

Liang,

Zhao,

Chen

et al. 2023

J Med Internet Res

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Background Developing effective and generalizable predictive models is critical for disease prediction and clinical decision-making, often requiring diverse samples to mitigate population bias and address algorithmic fairness. However, a major challenge is to retrieve learning models across multiple institutions without bringing in local biases and inequity, while preserving individual patients’ privacy at each site. Objective This study aims to understand the issues of bias and fairness in the machine learning process used in the predictive health care domain. We proposed a software architecture that integrates federated learning and blockchain to improve fairness, while maintaining acceptable prediction accuracy and minimizing overhead costs. Methods We improved existing federated learning platforms by integrating blockchain through an iterative design approach. We used the design science research method, which involves 2 design cycles (federated learning for bias mitigation and decentralized architecture). The design involves a bias-mitigation process within the blockchain-empowered federated learning framework based on a novel architecture. Under this architecture, multiple medical institutions can jointly train predictive models using their privacy-protected data effectively and efficiently and ultimately achieve fairness in decision-making in the health care domain. Results We designed and implemented our solution using the Aplos smart contract, microservices, Rahasak blockchain, and Apache Cassandra–based distributed storage. By conducting 20,000 local model training iterations and 1000 federated model training iterations across 5 simulated medical centers as peers in the Rahasak blockchain network, we demonstrated how our solution with an improved fairness mechanism can enhance the accuracy of predictive diagnosis. Conclusions Our study identified the technical challenges of prediction biases faced by existing predictive models in the health care domain. To overcome these challenges, we presented an innovative design solution using federated learning and blockchain, along with the adoption of a unique distributed architecture for a fairness-aware system. We have illustrated how this design can address privacy, security, prediction accuracy, and scalability challenges, ultimately improving fairness and equity in the predictive health care domain.

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A comparative evaluation of aggregation methods for machine learning over vertically partitioned data

Cited by 14 publications

References 28 publications

Trusted AI in Multi-agent Systems: An Overview of Privacy and Security for Distributed Learning

Trusted AI in Multi-agent Systems: An Overview of Privacy and Security for Distributed Learning

Improving the Quality Indicators of Multilevel Data Sampling Processing Models Based on Unsupervised Clustering

Architectural Design of a Blockchain-Enabled, Federated Learning Platform for Algorithmic Fairness in Predictive Health Care: Design Science Study

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