A Survey on Federated Learning PoisoningAttacks and Defenses

Liang, Junchuan; Rong, Wang

doi:10.21203/rs.3.rs-1900743/v1

Cited by 2 publications

(2 citation statements)

References 62 publications

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“…Data poisoning attacks and defenses have been demonstrated in the past on face recognition tasks [210]. These attacks also find a strong use case in the context of federated learning [213], [214], which are ripe for attacks via the data collection channel. Here, we find prior work on poisoning face recognition tasks in a federated learning context [215].…”

Section: ) Data Poisoning Attacksmentioning

confidence: 99%

A Comprehensive Survey on Backdoor Attacks and Their Defenses in Face Recognition Systems

Roux,

Bourbao,

Teglia

et al. 2024

IEEE Access

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Deep learning has significantly transformed face recognition, enabling the deployment of large-scale, state-of-the-art solutions worldwide. However, the widespread adoption of deep neural networks (DNNs) and the rise of Machine Learning as a Service emphasize the need for secure DNNs. This paper revisits the face recognition threat model in the context of DNN ubiquity and the common practice of outsourcing their training and hosting to third-parties. Here, we identify backdoor attacks as a significant threat to modern DNN-based face recognition systems (FRS). Backdoor attacks involve an attacker manipulating a DNN's training or deployment, injecting it with a stealthy and malicious behavior. Once the DNN has entered its inference stage, the attacker may activate the backdoor and compromise the DNN's intended functionality. Given the critical nature of this threat to DNN-based FRS, our paper comprehensively surveys the literature of backdoor attacks and defenses previously demonstrated on FRS DNNs. As a last point, we highlight potential vulnerabilities and unexplored areas in FRS security.

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Section: ) Data Poisoning Attacksmentioning

confidence: 99%

A Comprehensive Survey on Backdoor Attacks and Their Defenses in Face Recognition Systems

Roux,

Bourbao,

Teglia

et al. 2024

IEEE Access

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“…The goal of the first attack is to make the model unable to converge. The goal of the second attack is to use the global model for secret communication [61]. Milad Nasr et al designed white-box inference attacks for deep learning models and more specifically for FL use cases.…”

Section: Attacks On Federated Learningmentioning

confidence: 99%

Distributed Learning in the IoT–Edge–Cloud Continuum

Arzovs,

Judvaitis,

Nesenbergs

et al. 2024

MAKE

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The goal of the IoT–Edge–Cloud Continuum approach is to distribute computation and data loads across multiple types of devices taking advantage of the different strengths of each, such as proximity to the data source, data access, or computing power, while mitigating potential weaknesses. Most current machine learning operations are currently concentrated on remote high-performance computing devices, such as the cloud, which leads to challenges related to latency, privacy, and other inefficiencies. Distributed learning approaches can address these issues by enabling the distribution of machine learning operations throughout the IoT–Edge–Cloud Continuum by incorporating Edge and even IoT layers into machine learning operations more directly. Approaches like transfer learning could help to transfer the knowledge from more performant IoT–Edge–Cloud Continuum layers to more resource-constrained devices, e.g., IoT. The implementation of these methods in machine learning operations, including the related data handling security and privacy approaches, is challenging and actively being researched. In this article the distributed learning and transfer learning domains are researched, focusing on security, robustness, and privacy aspects, and their potential usage in the IoT–Edge–Cloud Continuum, including research on tools to use for implementing these methods. To achieve this, we have reviewed 145 sources and described the relevant methods as well as their relevant attack vectors and provided suggestions on mitigation.

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A Survey on Federated Learning PoisoningAttacks and Defenses

Cited by 2 publications

References 62 publications

A Comprehensive Survey on Backdoor Attacks and Their Defenses in Face Recognition Systems

A Comprehensive Survey on Backdoor Attacks and Their Defenses in Face Recognition Systems

Distributed Learning in the IoT–Edge–Cloud Continuum

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