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Using a Split-belt Treadmill to Evaluate Generalization of Human Locomotor Adaptation

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Published on: August 23, 2017

How to backdoor split learning.

Fangchao Yu¹, Lina Wang¹, Bo Zeng¹

¹Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan, 430072, China.

Neural Networks : the Official Journal of the International Neural Network Society

|October 2, 2023

Summary

This summary is machine-generated.

Split learning is vulnerable to backdoor attacks from both clients and servers. This research introduces novel attack frameworks, highlighting significant security risks in distributed machine learning applications.

Keywords:

Auxiliary model Backdoor attack Shadow model Split learning

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning Security

Background:

Split learning offers a flexible distributed framework, particularly for resource-constrained participants.
Existing security research primarily addresses inference attacks in split learning.
The separation of data/model control from usage rights raises security concerns.

Purpose of the Study:

To investigate and demonstrate the vulnerability of split learning to backdoor attacks.
To propose novel backdoor attack frameworks from both client and server perspectives.
To highlight potential security risks and inform secure deployment of split learning.

Main Methods:

Client-side attack: Inserting backdoor samples into local training data with adaptable labeling.
Server-side attack: Manipulating the client model's optimization direction and using an auxiliary model.
Evaluated attack effectiveness and impact on main task performance.

Main Results:

Demonstrated high attack accuracy for both client-side and server-side backdoor attacks.
Showcased that attacks do not compromise the performance of the main split learning task.
Validated the effectiveness of the auxiliary model in enhancing backdoor attack sensitivity.

Conclusions:

Split learning systems are susceptible to sophisticated backdoor attacks.
The proposed frameworks pose significant security threats, necessitating robust defense mechanisms.
This work underscores the urgent need for enhanced security protocols in split learning deployments.