Spatio-Temporal Split Learning for Privacy-Preserving Medical Platforms: Case Studies With COVID-19 CT, X-Ray, and Cholesterol Data

Ha, Yoo Jeong; Yoo, Minjae; Lee, Gusang; Jung, Soyi; Choi, Sae Won; Kim, Joongheon; Yoo, Seehwan

doi:10.1109/access.2021.3108455

Cited by 19 publications

(8 citation statements)

References 19 publications

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“…In recent years, SL has attracted much attention due to its capability to work well with low-end devices with limited computational capabilities. In addition, results obtained by split learning are comparable to models trained in a centralized setting on a healthcare dataset [ 17 , 18 , 19 ]. However, SL is only capable of dealing with one client at a time while training.…”

Section: Introductionmentioning

confidence: 97%

Performance and Information Leakage in Splitfed Learning and Multi-Head Split Learning in Healthcare Data and Beyond

Joshi

Thapa

Camtepe

et al. 2022

MPs

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Machine learning (ML) in healthcare data analytics is attracting much attention because of the unprecedented power of ML to extract knowledge that improves the decision-making process. At the same time, laws and ethics codes drafted by countries to govern healthcare data are becoming stringent. Although healthcare practitioners are struggling with an enforced governance framework, we see the emergence of distributed learning-based frameworks disrupting traditional-ML-model development. Splitfed learning (SFL) is one of the recent developments in distributed machine learning that empowers healthcare practitioners to preserve the privacy of input data and enables them to train ML models. However, SFL has some extra communication and computation overheads at the client side due to the requirement of client-side model synchronization. For a resource-constrained client side (hospitals with limited computational powers), removing such conditions is required to gain efficiency in the learning. In this regard, this paper studies SFL without client-side model synchronization. The resulting architecture is known as multi-head split learning (MHSL). At the same time, it is important to investigate information leakage, which indicates how much information is gained by the server related to the raw data directly out of the smashed data—the output of the client-side model portion—passed to it by the client. Our empirical studies examine the Resnet-18 and Conv1-D architecture model on the ECG and HAM-10000 datasets under IID data distribution. The results find that SFL provides 1.81% and 2.36% better accuracy than MHSL on the ECG and HAM-10000 datasets, respectively (for cut-layer value set to 1). Analysis of experimentation with various client-side model portions demonstrates that it has an impact on the overall performance. With an increase in layers in the client-side model portion, SFL performance improves while MHSL performance degrades. Experiment results also demonstrate that information leakage provided by mutual information score values in SFL is more than MHSL for ECG and HAM-10000 datasets by 2×10−5 and 4×10−3, respectively.

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Section: Introductionmentioning

confidence: 97%

Performance and Information Leakage in Splitfed Learning and Multi-Head Split Learning in Healthcare Data and Beyond

Joshi

Thapa

Camtepe

et al. 2022

MPs

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“…On the other hand, split learning (SL), first proposed in [3], [4], is an alternative solution to FL to cope with largesized models in a resource-efficient way [7], [8], [9]. A typical architecture of SL is constructed by splitting an entire deep neural network (DNN) model into two partitions, in a way that the upper model segment above the split-layer is stored at the server while the lower model segment is stored at each client.…”

Section: Introductionmentioning

confidence: 99%

Mix2SFL: Two-Way Mixup for Scalable, Accurate, and Communication-Efficient Split Federated Learning

Oh,

Nam,

Park

et al. 2024

IEEE Trans. Big Data

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In recent years, split learning (SL) has emerged as a promising distributed learning framework that can utilize big data in parallel without privacy leakage while reducing client-side computing resources. In the initial implementation of SL, however, the server serves multiple clients sequentially incurring high latency. Parallel implementation of SL can alleviate this latency problem, but existing Parallel SL algorithms compromise scalability due to its fundamental structural problem. To this end, our previous works have proposed two scalable Parallel SL algorithms, dubbed SGLR and LocFedMix-SL, by solving the aforementioned fundamental problem of the Parallel SL structure. In this article, we propose a novel Parallel SL framework, coined Mix2SFL, that can ameliorate both accuracy and communication-efficiency while still ensuring scalability. Mix2SFL first supplies more samples to the server through a manifold mixup between the smashed data uploaded to the server as in SmashMix of LocFedMix-SL, and then averages the split-layer gradient as in GradMix of SGLR, followed by local model aggregation as in SFL. Numerical evaluation corroborates that Mix2SFL achieves improved performance in both accuracy and latency compared to the state-of-the-art SL algorithm with scalability guarantees. Moreover, its convergence speed as well as privacy guarantee are validated through the experimental results.

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“…Split learning allows multiple participants to jointly train a federated model without encryption of intermediate results (intermediate computations of the cut layer), thus reducing the computation expense. SplitNN applies the idea of split learning to neural networks and has been used to analyze healthcare data (Poirot et al, 2019;Ha et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

GAN-based federated learning for label protection in binary classification

Han¹,

Guan²

2023

Preprint

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As an emerging technique, vertical federated learning collaborates with different data sources to jointly train a machine learning model without data exchange. However, federated learning is computationally expensive and inefficient in modeling due to complex encryption algorithms and secure computation protocols. Split learning offers an alternative solution to circumvent these challenges. Despite this, vanilla split learning still suffers privacy leakage. Here, we propose the Generative Adversarial Federated Model (GAFM), which integrates the vanilla split learning framework with the Generative Adversarial Network (GAN) for protection against label leakage from gradients in binary classification tasks. We compare our proposal to existing models, including Marvell, Max Norm, and SplitNN, on three publicly available datasets, where GAFM shows significant improvement regarding the trade-off between classification accuracy and label privacy protection. We also provide heuristic justification for why GAFM can improve over baselines and demonstrate that GAFM offers label protection through gradient perturbation compared to SplitNN.

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Spatio-Temporal Split Learning for Privacy-Preserving Medical Platforms: Case Studies With COVID-19 CT, X-Ray, and Cholesterol Data

Cited by 19 publications

References 19 publications

Performance and Information Leakage in Splitfed Learning and Multi-Head Split Learning in Healthcare Data and Beyond

Performance and Information Leakage in Splitfed Learning and Multi-Head Split Learning in Healthcare Data and Beyond

Mix2SFL: Two-Way Mixup for Scalable, Accurate, and Communication-Efficient Split Federated Learning

GAN-based federated learning for label protection in binary classification

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