Group Key Management for Scalable Federated Learning Secrecy
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Solution Overview
Problem
Existing federated learning systems face challenges in managing learning groups securely and scalably, particularly in edge network environments, with issues related to data privacy, model privacy, and single points of failure, which affect the security and efficiency of AI model training.
Innovation Solution
A group key management mechanism is employed to manage learning groups dynamically, using XOR operations and key derivation functions to generate and update group keys, ensuring secure data sharing and model training across distributed nodes, while maintaining forward and backward secrecy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If centralized group key management is used, then key management structure is simple and intuitive, but scalability deteriorates when participants frequently change and single point of failure problem occurs
Solution Approach 1:
The patent divides the centralized key management system into distributed key management units at each edge node. Each node independently manages its own keys and contributes to group key generation, eliminating the single centralized authority and enabling scalable participation without structural complexity increases.
Solution Approach 2:
The patent introduces a key contribution mechanism where each edge node acts as an intermediary, contributing its own key material to the group key generation process. This distributed intermediary approach replaces the centralized key server, allowing dynamic participant changes while maintaining security and scalability.
2Reliability
If group key is updated when participants change, then forward secrecy and backward secrecy are maintained, but key management overhead and time consumption increase
Solution Approach 1:
The patent pre-generates key material at each edge node before actual participation changes occur. When nodes join or leave, the pre-prepared key materials are quickly combined through cryptographic operations, avoiding time-consuming key generation during critical transitions and maintaining secrecy efficiently.
Solution Approach 2:
Each edge node independently performs key generation and update operations using its own key material and the group key contribution mechanism. This self-service approach eliminates the need for centralized key distribution and reduces key management overhead, allowing rapid key updates when participants change while maintaining forward and backward secrecy.
3Reliability
If distributed learning nodes are used for federated learning, then data privacy is improved, but model privacy and security management deteriorate
Solution Approach 1:
The patent combines data privacy protection with model privacy protection by using a unified group key management mechanism. The same cryptographic keys that protect data transmission between edge nodes and central server also protect model parameters and gradients during federated learning, simplifying security management while maintaining both data and model privacy.
Solution Approach 2:
The group key management system serves multiple functions simultaneously: it protects data transmission, secures model parameter exchange, authenticates participants, and enables secure key updates. This multi-functional approach reduces security management complexity compared to separate mechanisms for data and model protection.
Data Source
AI summary
Disclosed herein is an apparatus and method for federated learning based on group key management. The apparatus receives learning group participation request information from a learning node that intends to participate in a federated learning group, updates a group key required for the learning node to participate in federated learning using a key material generated by hashing the learning group participation request information, decrypts an encrypted local variable received from the learning node and thereby updates the same to a global variable, and transmits the global variable to the learning node after encrypting the same using the updated group key.