Distributed AI Model Attack Detection Through Layer-Based Inference
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Federated learning deployments are vulnerable to attacks on worker devices, which can compromise the global model and affect all devices, with existing methods requiring a central server for detection and not addressing worker-specific malfunctions.
Innovation Solution
A method for detecting attacks on worker devices in distributed AI systems by calculating local and distributed inferences, identifying inconsistencies through layer-based comparisons, and selectively aggregating non-compromised models to form a new global model, allowing decentralized detection and replacement of compromised models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a central server is used for attack detection in federated learning, then detection capability is provided, but system complexity and single point of failure risks increase
Solution Approach 1:
The attack detection functionality is segmented from the central server and distributed to individual worker devices. Each worker device independently performs inference calculations and detects attacks using its own computational resources, eliminating the need for a centralized detection authority while maintaining comprehensive detection coverage across the federated learning system.
Solution Approach 2:
Worker devices perform self-detection of attacks by calculating inferences using their local models and comparing results with inferences from other workers. This self-service mechanism allows each device to autonomously identify compromised models without requiring external verification from a central server, reducing system complexity and eliminating single points of failure.
2Productivity
If all worker models are aggregated for global model updates, then learning progress is maintained, but compromised models can poison the global model
Solution Approach 1:
The system implements feedback mechanisms where workers exchange inference results and compare outcomes. This feedback loop enables the identification of workers producing anomalous or malicious inferences, allowing the system to exclude compromised models from aggregation while maintaining efficient training progress through selective model updates.
Solution Approach 2:
The system dynamically changes aggregation parameters based on detected model reliability. Instead of uniformly aggregating all worker models, the system adjusts aggregation weights or excludes specific models based on their inferred trustworthiness, thereby maintaining training efficiency while protecting global model integrity from poisoning attacks.
3Ease of operation
If workers calculate inferences using their local models, then decentralized processing is achieved, but attacked workers can produce malicious inferences
Solution Approach 1:
The system merges multiple independent inference calculations from different workers into a collective verification process. By combining results and comparing outcomes across workers, the system maintains decentralized processing benefits while using the collective intelligence of the network to identify and neutralize malicious inferences produced by compromised workers.
Data Source
AI summary
A method for detecting an attack on a distributed artificial intelligence deployment comprising a plurality of worker devices. Each of the plurality of worker devices comprises a local machine learning model. Each local machine learning model comprises a plurality of layers. The method comprises calculating a first inference from first input data using a first machine learning model comprising layers of the plurality of layers of one or more of the local machine learning models and calculating additional inferences from the first input data using one or more additional machine learning models. Each of the additional machine learning models comprises at least one of the layers used in the first machine learning model and at least one layer from the pluralities of layers of the one or more local machine learning models that is not used by the first machine learning model. The method further comprises calculating differences between the first inference and each of the one or more additional inferences.