Semi-Supervised Federated Heterogeneous Transfer Learning

Feng, Siwei; Li, Boyang; Yu, Han; Liu, Yang; Yang, Qiang

doi:10.1016/j.knosys.2022.109384

Cited by 19 publications

(3 citation statements)

References 14 publications

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“…To deal with the widely existing overlapping data insufficiency problem across clients, Feng et al [51] proposed a Semi-Supervised Federated Heterogeneous Transfer Learning (SFHTL) framework that leverages unlabeled non-overlapping samples to reduce model overfitting. Compared with existing FTL methods, SFHTL can better expand the training set to improve the performance of the local model.…”

Section: Federated Transfer Learningmentioning

confidence: 99%

Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning

Ji,

Tan,

Saravirta

et al. 2023

Preprint

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Federated learning is a new learning paradigm that decouples data collection and model training via multi-party computation and model aggregation.As a flexible learning setting, federated learning has the potential to integrate with other learning frameworks.We conduct a focused survey of federated learning in conjunction with other learning algorithms. Specifically, we explore various learning algorithms to improve the vanilla federated averaging algorithm and review model fusion methods such as adaptive aggregation, regularization, clustered methods, and Bayesian methods. Following the emerging trends, we also discuss federated learning in the intersection with other learning paradigms, termed federated X learning, where X includes multitask learning, meta-learning, transfer learning, unsupervised learning, and reinforcement learning. This survey reviews the state of the art, challenges, and future directions.

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Section: Federated Transfer Learningmentioning

confidence: 99%

Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning

Ji,

Tan,

Saravirta

et al. 2023

Preprint

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“…Certain unlabelled samples containing valid information still remain. For the remaining unlabelled samples, semi-supervised learning [26][27][28] can take full advantage of these unlabelled samples and build a more accurate classification model with strong generalization ability. In semi-supervised learning, manifold regularization (MR) [29] framework is a commonly used framework.…”

Section: Introductionmentioning

confidence: 99%

A novel density ratio‐based batch active learning fault diagnosis method integrated with adaptive Laplacian graph trimming

Jiang

Zhu

et al. 2023

Can J Chem Eng

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In actual industrial processes, although a large number of original data are easy to obtain, only a few samples are effectively labelled, which is insufficient to construct a supervised fault diagnostic model. Facing the industrial demand of fault diagnosis, in this paper, a novel density ratio (DR)‐based batch active learning (BAL) fault diagnosis method integrated with adaptive Laplacian graph trimming (ALGT) method is proposed. First, under the active learning framework, a new index DR‐based on local reachability density (LRD) is proposed to search the low density and high uncertainty samples, in which the local outliers factor (LOF) is used to search the samples in low density region and the ratio of LRD and intra‐class LRD is calculated to search the samples with high uncertainty. Second, the samples are selected and manually labelled in batches according to the proposed index DR, and the labelled data set and the unlabelled data set are updated and reconstructed. Third, based on the reconstructed labelled dataset and remaining unlabelled dataset, a semi‐supervised classifier ALGT is constructed for fault diagnosis. In ALGT, the Laplacian weighted graph is initialized and iteratively optimized by ALGT. Finally, the proposed DR‐based BAL‐ALGT (DRBAL‐ALGT) fault diagnosis method is verified by the Tennessee Eastman process (TEP) and applied to grid‐connected photovoltaic systems (GPVS). The experimental results show that the proposed DRBAL‐ALGT method can achieve higher accuracy for fault diagnosis.

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“…In the community of heterogeneous transfer learning, in order to establish the connection between source and target domains with different feature dimensions, a small number of accurately labeled samples in the target domain is normally required, which contributes to semi-supervised learning [ 24 , 25 ]. In the field of FIPS—especially in the online positioning phase, and different from image and text classifications—obtaining accurately labeled samples is often labor-consuming, rendering existing semi-supervised heterogeneous transfer algorithms that are not practical for FIPS in the presence of the environment or collection device changes.…”

Section: Introductionmentioning

confidence: 99%

Co-Occurrence Fingerprint Data-Based Heterogeneous Transfer Learning Framework for Indoor Positioning

Huang

Guo

et al. 2022

Sensors

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Distribution discrepancy is an intrinsic challenge in existing fingerprint-based indoor positioning system(s) (FIPS) due to real-time environmental variations; thus, the positioning model needs to be reconstructed frequently based on newly collected training data. However, it is expensive or impossible to collect adequate training samples to reconstruct the fingerprint database. Fortunately, transfer learning has proven to be an effective solution to mitigate the distribution discrepancy, enabling us to update the positioning model using newly collected training data in real time. However, in practical applications, traditional transfer learning algorithms no longer act well to feature space heterogeneity caused by different types or holding postures of fingerprint collection devices (such as smartphones). Moreover, current heterogeneous transfer methods typically require enough accurately labeled samples in the target domain, which is practically expensive and even unavailable. Aiming to solve these problems, a heterogeneous transfer learning framework based on co-occurrence data (HTL-CD) is proposed for FIPS, which can realize higher positioning accuracy and robustness against environmental changes without reconstructing the fingerprint database repeatedly. Specifically, the source domain samples are mapped into the feature space in the target domain, then the marginal and conditional distributions of the source and target samples are aligned in order to minimize the distribution divergence caused by collection device heterogeneity and environmental changes. Moreover, the utilized co-occurrence fingerprint data enables us to calculate correlation coefficients between heterogeneous samples without accurately labeled target samples. Furthermore, by resorting to the adopted correlation restriction mechanism, more valuable knowledge will be transferred to the target domain if the source samples are related to the target ones, which remarkably relieves the “negative transfer" issue. Real-world experimental performance implies that, even without accurately labeled samples in the target domain, the proposed HTL-CD can obtain at least 17.15% smaller average localization errors (ALEs) than existing transfer learning-based positioning methods, which further validates the effectiveness and superiority of our algorithm.

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Semi-Supervised Federated Heterogeneous Transfer Learning

Cited by 19 publications

References 14 publications

Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning

Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning

A novel density ratio‐based batch active learning fault diagnosis method integrated with adaptive Laplacian graph trimming

Co-Occurrence Fingerprint Data-Based Heterogeneous Transfer Learning Framework for Indoor Positioning

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