Federated Learning Security Entity for Malicious Model Update Screening
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Solution Overview
Problem
Existing federated learning systems in communication networks face challenges in ensuring data security and privacy, particularly in scenarios where sensitive data is shared across multiple devices without a centralized control point.
Innovation Solution
Implementing enhanced security measures in federated learning operations through the use of cryptographic techniques, such as homomorphic encryption and secure aggregation protocols, to protect data integrity and confidentiality during model training across distributed devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If federated learning is implemented across distributed devices without centralized control, then data privacy and security are improved, but system reliability and security monitoring become more difficult
Solution Approach 1:
A security entity is introduced as an intermediary component within the federated learning system. This security entity receives model updates from multiple devices, performs security assessments on each update, and determines whether to allow aggregation. The security entity acts as a mediator that maintains security without requiring centralized control of the learning process itself, thus improving data security while managing system complexity through a dedicated specialized component.
2Measurement precision
If security assessment of model updates is performed, then detection of malicious updates is improved, but processing time and computational overhead increase
Solution Approach 1:
The security entity performs security assessments on model updates before they are aggregated into the global model. By conducting security checks in advance (preliminary action) on individual device updates, the system can identify and reject malicious updates before they contaminate the global model, improving detection accuracy while minimizing time loss by processing security checks in parallel with the distributed learning process.
3Reliability
If multiple devices collaborate in federated learning, then model training robustness is improved, but vulnerability to malicious updates increases
Solution Approach 1:
The security entity implements a feedback mechanism where security assessment results of model updates are used to control the aggregation process. When a malicious update is detected, the security entity provides feedback to prevent its inclusion in the global model. This feedback loop maintains the distributed collaborative training robustness while counteracting the vulnerability to malicious updates by dynamically adjusting aggregation based on security assessments.
Data Source
AI summary
The disclosure pertains to methods and apparatus for federated learning (FL) artificial intelligence machine learning (AIML) implemented in a first security entity associated with a Wireless Transmit/Receive Unit (WTRU), the method comprising: obtaining, by the first security entity, first information indicating security assessment data of the WTRU in a federated learning network; determining, by the first security entity, based on the security assessment data, an indication of a security vulnerability of a trained FL AIML intermediate model; sending based on the determined indication of the security vulnerability, by the first security entity to a second security entity associated with a network node, second information indicating the trained FL AIML intermediate model; and sending, by the first security entity to the network node, third information associated with a training of the trained FL AIML intermediate model.