SecureBoost: A Lossless Federated Learning Framework

Cheng, Kewei; Fan, Tao; Jin, Yilun; Liu, Yang; Chen, Tianjian; Papadopoulos, Dimitrios; Yang, Qiang

doi:10.1109/mis.2021.3082561

Cited by 326 publications

(179 citation statements)

References 20 publications

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“…Yang et al [206] proposed a vertical FL framework for a logistic regress model without a third-party coordinator. Cheng et al [27] proposed a lossless vertical FL method, which could enable clients to train gradient boosting decision trees in a collaborative manner. Liu et al [112] proposed a vertical FL framework based on a block coordinate gradient descent algorithm, in which each client locally conducts more than one gradient updates before sending the local model information to the other clients.…”

Section: Vertical Flmentioning

confidence: 99%

A Comprehensive Survey of Privacy-preserving Federated Learning

2021

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The past four years have witnessed the rapid development of federated learning (FL). However, new privacy concerns have also emerged during the aggregation of the distributed intermediate results. The emerging privacy-preserving FL (PPFL) has been heralded as a solution to generic privacy-preserving machine learning. However, the challenge of protecting data privacy while maintaining the data utility through machine learning still remains. In this article, we present a comprehensive and systematic survey on the PPFL based on our proposed 5W-scenario-based taxonomy. We analyze the privacy leakage risks in the FL from five aspects, summarize existing methods, and identify future research directions.

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Section: Vertical Flmentioning

confidence: 99%

A Comprehensive Survey of Privacy-preserving Federated Learning

2021

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“…They apply either hybrid secure multi-party computation (SMPC) protocol or homomorphic encryption (HE) [35] to protect privacy in model training and inference. [3] proposes a secure federated tree-boosting (SecureBoost) approach in the VFL setting. It enables participating parties with different features to build a set of boosting trees collaboratively and proves that the SecureBoost provides the same level of accuracy as its non-privacy-preserving centralized counterparts.…”

Section: Related Workmentioning

confidence: 99%

FedCVT: Semi-supervised Vertical Federated Learning with Cross-view Training

Kang¹,

Liu

Liang³

2022

ACM Trans. Intell. Syst. Technol.

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Federated learning allows multiple parties to build machine learning models collaboratively without exposing data. In particular, vertical federated learning (VFL) enables participating parties to build a joint machine learning model based upon distributed features of aligned samples. However, VFL requires all parties to share a sufficient amount of aligned samples. In reality, the set of aligned samples may be small, leaving the majority of the non-aligned data unused. In this paper, we propose Federated Cross-View Training (FedCVT), a semi-supervised learning approach that improves the performance of the VFL model with limited aligned samples. More specifically, FedCVT estimates representations for missing features, predicts pseudo-labels for unlabeled samples to expand the training set, and trains three classifiers jointly based upon different views of the expanded training set to improve the VFL model’s performance. FedCVT does not require parties to share their original data and model parameters, thus preserving data privacy. We conduct experiments on NUS-WIDE, Vehicle, and CIFAR10 datasets. The experimental results demonstrate that FedCVT significantly outperforms vanilla VFL that only utilizes aligned samples. Finally, we perform ablation studies to investigate the contribution of each component of FedCVT to the performance of FedCVT.

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“…Current research also proposed some decentralized FL approaches that did not rely on blockchain. For instance, Cheng et al [36] proposed a lossless tree-boosting system for vertical FL. It could protect the privacy of clients without reducing the accuracy of the model.…”

Section: Decentralized Federated Learningmentioning

confidence: 99%

“…However, such two completely different institutions can contribute their data to produce a global model in a vertical manner, though they are prohibited from communicating and do not know what information each other provided. Cheng et al [36] studied vertical FL and proposed a privacy-preserving system called SecureBoost. This system compiled information of multiple groups with common user samples but different feature sets to enhance model training.…”

mentioning

confidence: 99%

Applying Federated Learning in Software-Defined Networks: A Survey

Liao

Zhi

et al. 2022

Symmetry

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Federated learning (FL) is a type of distributed machine learning approacs that trains global models through the collaboration of participants. It protects data privacy as participants only contribute local models instead of sharing private local data. However, the performance of FL highly relies on the number of participants and their contributions. When applying FL over conventional computer networks, attracting more participants, encouraging participants to contribute more local resources, and enabling efficient and effective collaboration among participants become very challenging. As software-defined networks (SDNs) enable open and flexible networking architecture with separate control and data planes, SDNs provide standardized protocols and specifications to enable fine-grained collaborations among devices. Applying FL approaches over SDNs can take use such advantages to address challenges. A SDN control plane can have multiple controllers organized in layers; the controllers in the lower layer can be placed in the network edge to deal with the asymmetries in the attached switches and hosts, and the controller in the upper layer can supervise the whole network centrally and globally. Applying FL in SDNs with a layered-distributed control plane may be able to protect the data privacy of each participant while improving collaboration among participants to produce higher-quality models over asymmetric networks. Accordingly, this paper aims to make a comprehensive survey on the related mechanisms and solutions that enable FL in SDNs. It highlights three major challenges, an incentive mechanism, privacy and security, and model aggregation, which affect the quality and quantity of participants, the security and privacy in model transferring, and the performance of the global model, respectively. The state of the art in mechanisms and solutions that can be applied to address such challenges in the current literature are categorized based on the challenges they face, followed by suggestions of future research directions. To the best of our knowledge, this work is the first effort in surveying the state of the art in combining FL with SDNs.

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SecureBoost: A Lossless Federated Learning Framework

Cited by 326 publications

References 20 publications

A Comprehensive Survey of Privacy-preserving Federated Learning

A Comprehensive Survey of Privacy-preserving Federated Learning

FedCVT: Semi-supervised Vertical Federated Learning with Cross-view Training

Applying Federated Learning in Software-Defined Networks: A Survey

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