SplitGuard

Erdogan, Ege; Küpçü, Alptekin; Çiçek, A. Ercüment

doi:10.1145/3559613.3563198

Cited by 12 publications

(2 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The active attack is more flexible than the passive attack but is more easily detected by the client. Erdogan et al (Erdogan, Küpc ¸ü, and Cicek 2022) argue that the weight update direction of the clients in FSHA is independent of the main task. Thus, clients can submit a small amount of erroneous data during the training process to observe the variations in gradient information returned by the server, enabling them to detect any malicious behavior on the server side.…”

Section: Related Workmentioning

confidence: 99%

Chronic Poisoning: Backdoor Attack against Split Learning

Yu,

Zeng,

Zhao

et al. 2024

AAAI

View full text Add to dashboard Cite

Split learning is a computing resource-friendly distributed learning framework that protects client training data by splitting the model between the client and server. Previous work has proved that split learning faces a severe risk of privacy leakage, as a malicious server can recover the client's private data by hijacking the training process. In this paper, we first explore the vulnerability of split learning to server-side backdoor attacks, where our goal is to compromise the model's integrity. Since the server-side attacker cannot access the training data and client model in split learning, the traditional poisoning-based backdoor attack methods are no longer applicable. Therefore, constructing backdoor attacks in split learning poses significant challenges. Our strategy involves the attacker establishing a shadow model on the server side that can encode backdoor samples and guiding the client model to learn from this model during the training process, thereby enabling the client to acquire the same capability. Based on these insights, we propose a three-stage backdoor attack framework named SFI. Our attack framework minimizes assumptions about the attacker's background knowledge and ensures that the attack process remains imperceptible to the client. We implement SFI on various benchmark datasets, and extensive experimental results demonstrate its effectiveness and generality. For example, success rates of our attack on MNIST, Fashion, and CIFAR10 datasets all exceed 90%, with limited impact on the main task.

show abstract

Section: Related Workmentioning

confidence: 99%

Chronic Poisoning: Backdoor Attack against Split Learning

Yu,

Zeng,

Zhao

et al. 2024

AAAI

View full text Add to dashboard Cite

show abstract

“…Their approach modified model training to reduce data leakage while maintaining accuracy. SplitGuard [174], [175] detected and mitigated hijacking attacks in SL. UnSplit [176] studied data oblivious model inversion, model stealing, and label inference attacks against SL.…”

Section: ) Research On Handling Privacy Issues In Slmentioning

confidence: 99%

Decentralized Learning in Healthcare: A Review of Emerging Techniques

2023

View full text Add to dashboard Cite

Recent developments in deep learning have contributed to numerous success stories in healthcare. The performance of a deep learning model generally improves with the size of the training data. However, there are privacy, ownership, and regulatory issues that prevent combining medical data into traditional centralized storage. Decentralized learning approaches enable collaborative model training by distributing the learning process among several nodes or devices. Conceptually, decentralized learning builds on earlier work in distributed optimization, but the focus of this paper is on recent and emerging techniques such as Federated Learning (FL), Split Learning (SL), and hybrid Split-Federated Learning (SFL). With common, universal deep learning models and centralized aggregator servers, FL overcomes the difficulties of centralized training. Additionally, patient data remains at the local party, upholding the security and anonymity of the data. SL enables machine learning without directly accessing data on clients or end devices. It further enhances privacy in a decentralized setting and mitigates clients' storage issues. In this survey, we first provide a contemporary survey of FL, SL, and SFL approaches. Second, we discuss their state-ofthe-art applications in healthcare, particularly in medical image analysis. Third, we review these emerging decentralized learning approaches under challenging conditions such as statistical and system heterogeneity, privacy preservation, communication efficiency, fairness, etc. Then, we address existing approaches to tackle these challenges. We detail unique complications related to healthcare applications including data, privacy and security, and communication challenges. Finally, we outline potential areas for further research on emerging decentralized learning techniques in healthcare, including developing personalized models, reducing bias, incorporating hybrid non-IID features, hyperparameter tuning, developing sufficient incentive mechanisms, and incorporating domain expertise knowledge.

show abstract