Secure Federated Learning Authentication via Bilevel Verification
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Solution Overview
Problem
In secure federated learning systems, the risk of malicious edge devices injecting false data or performing reverse engineering attacks poses a threat to data privacy and security, especially when untrusted servers are involved, as existing approaches lack robust authentication mechanisms.
Innovation Solution
A bilevel authentication method is implemented, where edge devices store a device-specific identifier in secured memory blocks, using hardware isolation, and a secure multiparty computation protocol is employed to protect the integrity of edge devices, ensuring only authentic updates are aggregated into a federated ANN model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If federated learning is implemented without authentication mechanisms, then system complexity is reduced and ease of operation is improved, but security and data privacy are compromised due to malicious edge devices
Solution Approach 1:
The system performs authentication of edge devices before allowing them to participate in federated learning. Device-specific identifiers are verified in advance, and only authenticated devices are permitted to submit model updates. This preliminary authentication prevents malicious devices from injecting false data while maintaining system security.
Solution Approach 2:
A secure multiparty computation protocol is introduced as an intermediary mechanism between edge devices and the central server. This protocol enables verification of device authenticity and integrity of updates without requiring direct trust between devices and the server, thus enhancing security while managing complexity through a standardized intermediary layer.
2Reliability
If authentication mechanisms are implemented to verify edge devices, then security against malicious actors is improved, but system complexity and computational overhead increase
Solution Approach 1:
The authentication system is segmented into distinct components: device-specific identifier storage in secured memory blocks, hardware isolation mechanisms, and verification protocols. This segmentation allows each component to be optimized independently and simplifies the overall system architecture by dividing the authentication function into manageable segments.
Solution Approach 2:
Edge devices perform self-authentication using their own device-specific identifiers stored in secured memory. The hardware isolation mechanisms automatically verify the authenticity of these identifiers without requiring external intervention, enabling self-service authentication that reduces system complexity while maintaining security.
3Reliability
If secured memory blocks with hardware isolation are used to store device identifiers, then authentication reliability is improved, but device complexity and manufacturing cost increase
Solution Approach 1:
Device-specific identifiers are pre-provisioned into secured memory blocks during the manufacturing process. This preliminary action ensures that each device has a unique, hardware-bound identifier before deployment, enhancing authentication reliability while allowing standard manufacturing processes to be used without significant modifications.
Data Source
AI summary
Authentication based secure federated learning can be beneficial when updates to an initial artificial neural network (ANN) model generated by edge devices are to be aggregated into a federated ANN model. The updates can be generated by the edge devices after training the initial ANN locally. The edge devices can encrypt the updates using edge device-specific identifiers and transmit the encrypted updates to a server. The server can verify the authenticity of the updates, decrypt the updates, and aggregate the updates into the federated ANN model. The aggregation can be performed according to a secure multiparty computation protocol.