Adaptive Vertical Federated Learning on Unbalanced Features

Zhang, Jie; Guo, Song; Qu, Zhihao; Zeng, Deze; Wang, Haozhao; Liu, Qifeng; Zomaya, Albert Y.

doi:10.1109/tpds.2022.3178443

Cited by 14 publications

(5 citation statements)

References 20 publications

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“…This characteristic has to be managed by applications dealing with Artificial Intelligence for the automated interpretation of the image content. In case the image analysis method could not manage unbalanced datasets 61 , 62 , data augmentation approaches could be used for generating new reliable individuals starting from the classes tagged in the dataset 63 – 65 .…”

Section: Usage Notesmentioning

confidence: 99%

Image dataset for benchmarking automated fish detection and classification algorithms

Francescangeli

Marini

Martinez³

et al. 2023

Sci Data

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Multiparametric video-cabled marine observatories are becoming strategic to monitor remotely and in real-time the marine ecosystem. Those platforms can achieve continuous, high-frequency and long-lasting image data sets that require automation in order to extract biological time series. The OBSEA, located at 4 km from Vilanova i la Geltrú at 20 m depth, was used to produce coastal fish time series continuously over the 24-h during 2013–2014. The image content of the photos was extracted via tagging, resulting in 69917 fish tags of 30 taxa identified. We also provided a meteorological and oceanographic dataset filtered by a quality control procedure to define real-world conditions affecting image quality. The tagged fish dataset can be of great importance to develop Artificial Intelligence routines for the automated identification and classification of fishes in extensive time-lapse image sets.

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Section: Usage Notesmentioning

confidence: 99%

Image dataset for benchmarking automated fish detection and classification algorithms

Francescangeli

Marini

Martinez³

et al. 2023

Sci Data

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“…Research gap Recent research has addressed the problem of analyzing [3,4,13] and/ or reducing [14][15][16][17][18][19][20][21][22][23][24] the energy demand and carbon footprint of FL applications [1]. However, most of this research has focused on HFL settings, paying no attention to the challenging case of VFL applications, where the parties are obliged to cooperate tightly across all training iterations by exchanging derived information concerning the parameters/gradients of their local models.…”

Section: Introductionmentioning

confidence: 99%

Efficiently approaching vertical federated learning by combining data reduction and conditional computation techniques

Folino,

Pisani

et al. 2024

J Big Data

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In this paper, a framework based on a sparse Mixture of Experts (MoE) architecture is proposed for the federated learning and application of a distributed classification model in domains (like cybersecurity and healthcare) where different parties of the federation store different subsets of features for a number of data instances. The framework is designed to limit the risk of information leakage and computation/communication costs in both model training (through data sampling) and application (leveraging the conditional-computation abilities of sparse MoEs). Experiments on real data have shown the proposed approach to ensure a better balance between efficiency and model accuracy, compared to other VFL-based solutions. Notably, in a real-life cybersecurity case study focused on malware classification (the KronoDroid dataset), the proposed method surpasses competitors even though it utilizes only 50% and 75% of the training set, which is fully utilized by the other approaches in the competition. This method achieves reductions in the rate of false positives by 16.9% and 18.2%, respectively, and also delivers satisfactory results on the other evaluation metrics. These results showcase our framework’s potential to significantly enhance cybersecurity threat detection and prevention in a collaborative yet secure manner.

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“…Federated Learning (FL) is a concept to analyze datasets, which are distributed over different devices that are connected with a central station [17]. It can be divided in horizontal and vertical FL [18].…”

Section: Federated Learningmentioning

confidence: 99%

“…This means that some participants of the SC are able to contribute more to the whole system than others. The issue of this is that a federated learning architecture isn't able to balance these different feature relevance, without exchanging of datasets [17]. It should be mentioned that Zhang, et.al.…”

Section: Federated Learningmentioning

confidence: 99%

Development of a Prototype for a Process Support and Analysis Platform for Small and Medium-sized Enterprises

Kallisch¹,

Wunck²

EPiC Series in Computing

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This paper presents a prototype for an information exchange system, which allows information exchange between companies without actually sharing data. First, the need for such an intercompany exchange platform is explained and the value for supply chains resulting from such a platform is described. A literature review presents the existing concepts and techniques contributing to the development of an architecture. Finally. The information exchange concept and the prototype implementation are explained in detail.*

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Adaptive Vertical Federated Learning on Unbalanced Features

Cited by 14 publications

References 20 publications

Image dataset for benchmarking automated fish detection and classification algorithms

Image dataset for benchmarking automated fish detection and classification algorithms

Efficiently approaching vertical federated learning by combining data reduction and conditional computation techniques

Development of a Prototype for a Process Support and Analysis Platform for Small and Medium-sized Enterprises

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