Quantum-Secure Federated Learning for Unstable Distributed AI Networks
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Solution Overview
Problem
Classical federated learning (FL) faces challenges in heterogeneous networks due to frequent device disconnections, biased training results, and optimizing resource consumption, particularly in wireless technologies like 6G and Open Radio Access Network (O-RAN).
Innovation Solution
Implementing a system architecture that leverages software-defined networking (SDN) and quantum computing-enabled fog servers to detect real-time connectivity, adjust model biases, and provide on-demand computation services, ensuring secure and reliable FL even in heterogeneous networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If classical federated learning is implemented in heterogeneous networks, then device participation is enabled, but connectivity failures and biased training results occur due to frequent disconnections
Solution Approach 1:
The system performs preliminary actions by pre-establishing quantum entanglement connections between devices before federated learning training begins. This allows the system to proactively set up secure quantum channels in advance, so that when devices connect and disconnect during training, the quantum security framework is already in place to handle these transitions smoothly, preventing connectivity failures from compromising training accuracy
Solution Approach 2:
The patent introduces a quantum key distribution intermediary layer that mediates between heterogeneous devices and the federated learning server. This intermediary manages quantum entanglement resources and distributes quantum keys to participating devices, enabling reliable and secure communication even as devices dynamically join and leave the network, thus resolving the contradiction between device adaptability and training reliability
2Measurement precision
If quantum computing resources are allocated to federated learning, then computational accuracy is improved, but resource consumption increases
Solution Approach 1:
The system applies local quality by allocating quantum computing resources selectively to specific federated learning tasks that require high computational accuracy, rather than uniformly applying quantum resources to all training operations. Classical computing resources handle routine training tasks, while quantum resources are concentrated on computationally intensive operations like complex model training or security-critical computations, optimizing the balance between accuracy and resource consumption
Solution Approach 2:
The patent implements parameter changes by dynamically adjusting the allocation of quantum versus classical computing resources based on task requirements, network conditions, and resource availability. The system can switch between quantum and classical computational modes, adjusting computational parameters to achieve required accuracy levels while minimizing resource consumption, thereby resolving the contradiction between computational precision and energy use
3Reliability
If quantum key distribution is implemented for secure communication, then security is enhanced, but system complexity increases
Solution Approach 1:
The patent introduces quantum key distribution intermediaries and management servers that handle the complexity of quantum entanglement establishment and key distribution. These intermediaries abstract the quantum complexity from end devices, providing simple APIs and automated key management, thereby enhancing communication security while managing system complexity through centralized quantum resource management
Solution Approach 2:
The system implements multi-functionality by designing quantum key distribution infrastructure that serves multiple purposes: securing federated learning communications, managing device authentication, and providing quantum-secure key distribution for various network operations. This universal quantum security framework reduces overall system complexity by consolidating security functions rather than implementing separate security mechanisms for each operation
Data Source
AI summary
Aspects of the subject disclosure may include, for example, transmitting, to a server, a request to participate in FL for an AI model, where a cloud system operates a first instance of the model and the device operates a second instance of the model, receiving, from the server, information relating to training of the second instance of the model, determining that computation(s) associated with the training are to be performed by a quantum computer, causing at least a portion of a local dataset to be provided to the quantum computer to perform the computation(s), receiving, from the quantum computer, output(s) of the computation(s), providing the output(s) to the server to enable aggregation of the output(s) with output(s) of other device(s) involved in the FL, obtaining aggregated data from the server, and utilizing the aggregated data to update the second instance of the model. Other embodiments are disclosed.