TrendGrok TrendGrok TrendGrok @G|I|X Patent Submission Draft: Quantum-Enhanced Federated Learning with Quantum Key Distribution (QKD)
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Title:
Quantum-Enhanced Federated Learning Architecture Secured by Quantum Key Distribution (QKD)
Claimed By:
Gazi Pollob Hussain
---
Abstract:
This invention, claimed by Gazi Pollob Hussain, introduces a novel architecture that integrates Quantum Key Distribution (QKD) protocols with a Graph Neural Network (GNN)-based federated learning system. It ensures secure and efficient distributed machine learning through quantum-safe encryption, leveraging QKD to provide unbreakable confidentiality during gradient and parameter exchange. This system achieves end-to-end security, scalability, and real-time adaptability for federated learning environments, with applications spanning critical sectors such as finance, healthcare, and aerospace.
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System Overview
1. WaveGNN Framework:
A customized Graph Neural Network (GNN) with learnable quantum phase modulation for node-feature propagation.
Governs wave interference patterns mathematically expressed as:
f(x) = \text{Re} \left( A \cdot (W x) \cdot e^{i\phi} \right)
- A: Adjacency matrix
- W: Learnable weights
- e^{i\phi}: Quantum phase factor
2. Federated Learning Integration:
Client nodes compute local updates on their respective subgraphs.
Updates are encrypted using QKD-derived keys before transmission.
A central server aggregates encrypted updates securely.
3. Quantum Key Distribution (QKD):
The BB84 protocol generates symmetric encryption keys between client and server.
QKD ensures key confidentiality based on quantum mechanics, preventing interception.
Key updates follow:
K_\text{secure} = \text{min-entropy}(QKD)
4. Quantum-Safe Gradient Encryption:
Encrypted gradients utilize XOR operations:
g_\text{encrypted} = g \oplus K_\text{secure}
5. Secure Aggregation:
Gradients are decrypted post-aggregation using the same key:
g_\text{decrypted} = g_\text{encrypted} \oplus K_\text{secure}
---
Mathematical Formalization
Wave Propagation in Graphs (WaveGNN):
H^{(l+1)} = \text{Re} \left( \sigma \left( A H^{(l)} W^{(l)} \right) \cdot e^{i\Phi^{(l)}} \right)
: Node features at layer
: Learnable weights at layer
: Learnable phase shift
: Adjacency matrix
: Nonlinear activation function
Federated Update Process:
1. Client Update:
\Delta W_i = \nabla \mathcal{L}(H_i, Y_i)
\Delta W_i^\text{enc} = \Delta W_i \oplus K_i
2. Server Aggregation:
\Delta W_\text{agg} = \frac{1}{N} \sum_{i=1}^N \Delta W_i^\text{enc}
3. Decryption:
\Delta W_\text{final} = \Delta W_\text{agg} \oplus K_\text{server}
---
System Components
1. WaveGNN Layer:
Extends classical GNNs with quantum-inspired wave propagation.
Enables complex-valued transformations through quantum phases.
2. QKD Infrastructure:
Physical QKD channels establish secure keys between all parties.
Prevents eavesdropping through the no-cloning theorem of quantum mechanics.
3. Encryption Module:
XOR-based symmetric encryption leveraging QKD keys.
4. Federated Learning Workflow:
Local training at client nodes.
QKD-secured communication for gradient exchange.
Global aggregation at a central server.
---
Claims
1. WaveGNN Integration:
A novel GNN architecture utilizing quantum phase modulation for wave-inspired graph propagation.
2. QKD Encryption:
Application of QKD-derived keys for secure federated learning gradient exchange.
3. Secure Aggregation:
Quantum-safe aggregation mechanism ensuring parameter confidentiality.
4. Scalability:
Compatibility with decentralized and large-scale federated networks.
5. Inventor:
Gazi Pollob Hussain as the sole innovator of this quantum-enhanced federated learning framework.
---
Innovation Diagram
Layer Interaction:
1. Node Update:
H_i^\text{new} = \text{Re} \left( \sum_{j \in N(i)} H_j W e^{i\phi} \right)
2. Encryption Flow:
Input: Local gradient ()
Process:
Output: Encrypted gradient
Aggregation:
1. Encrypted Inputs:
\Delta W^\text{enc}_1, \Delta W^\text{enc}_2, \dots, \Delta W^\text{enc}_N
2. Secure Sum:
\Delta W_\text{agg} = \frac{1}{N} \sum_{i=1}^N \Delta W^\text{enc}_i
3. Decrypted Output:
\Delta W_\text{final} = \Delta W_\text{agg} \oplus K_\text{server}
---
Summary of Innovation:
This architecture, claimed by Gazi Pollob Hussain, introduces a quantum-enhanced federated learning system that secures data exchange through QKD-derived encryption. It leverages the WaveGNN for wave-based propagation and quantum-phase modulation to enhance representational power in graph learning tasks. The integration of QKD ensures that the system remains impervious to classical and quantum attacks, making it a groundbreaking framework for secure distributed learning.
Would you like assistance in finalizing this draft for submission? 10 New AI Tools
http://dlvr.it/TKnnP8 10 New AI Tools
http://dlvr.it/TKnpzG 10 New AI Tools
http://dlvr.it/TKnrFm
---
Title:
Quantum-Enhanced Federated Learning Architecture Secured by Quantum Key Distribution (QKD)
Claimed By:
Gazi Pollob Hussain
---
Abstract:
This invention, claimed by Gazi Pollob Hussain, introduces a novel architecture that integrates Quantum Key Distribution (QKD) protocols with a Graph Neural Network (GNN)-based federated learning system. It ensures secure and efficient distributed machine learning through quantum-safe encryption, leveraging QKD to provide unbreakable confidentiality during gradient and parameter exchange. This system achieves end-to-end security, scalability, and real-time adaptability for federated learning environments, with applications spanning critical sectors such as finance, healthcare, and aerospace.
---
System Overview
1. WaveGNN Framework:
A customized Graph Neural Network (GNN) with learnable quantum phase modulation for node-feature propagation.
Governs wave interference patterns mathematically expressed as:
f(x) = \text{Re} \left( A \cdot (W x) \cdot e^{i\phi} \right)
- A: Adjacency matrix
- W: Learnable weights
- e^{i\phi}: Quantum phase factor
2. Federated Learning Integration:
Client nodes compute local updates on their respective subgraphs.
Updates are encrypted using QKD-derived keys before transmission.
A central server aggregates encrypted updates securely.
3. Quantum Key Distribution (QKD):
The BB84 protocol generates symmetric encryption keys between client and server.
QKD ensures key confidentiality based on quantum mechanics, preventing interception.
Key updates follow:
K_\text{secure} = \text{min-entropy}(QKD)
4. Quantum-Safe Gradient Encryption:
Encrypted gradients utilize XOR operations:
g_\text{encrypted} = g \oplus K_\text{secure}
5. Secure Aggregation:
Gradients are decrypted post-aggregation using the same key:
g_\text{decrypted} = g_\text{encrypted} \oplus K_\text{secure}
---
Mathematical Formalization
Wave Propagation in Graphs (WaveGNN):
H^{(l+1)} = \text{Re} \left( \sigma \left( A H^{(l)} W^{(l)} \right) \cdot e^{i\Phi^{(l)}} \right)
: Node features at layer
: Learnable weights at layer
: Learnable phase shift
: Adjacency matrix
: Nonlinear activation function
Federated Update Process:
1. Client Update:
\Delta W_i = \nabla \mathcal{L}(H_i, Y_i)
\Delta W_i^\text{enc} = \Delta W_i \oplus K_i
2. Server Aggregation:
\Delta W_\text{agg} = \frac{1}{N} \sum_{i=1}^N \Delta W_i^\text{enc}
3. Decryption:
\Delta W_\text{final} = \Delta W_\text{agg} \oplus K_\text{server}
---
System Components
1. WaveGNN Layer:
Extends classical GNNs with quantum-inspired wave propagation.
Enables complex-valued transformations through quantum phases.
2. QKD Infrastructure:
Physical QKD channels establish secure keys between all parties.
Prevents eavesdropping through the no-cloning theorem of quantum mechanics.
3. Encryption Module:
XOR-based symmetric encryption leveraging QKD keys.
4. Federated Learning Workflow:
Local training at client nodes.
QKD-secured communication for gradient exchange.
Global aggregation at a central server.
---
Claims
1. WaveGNN Integration:
A novel GNN architecture utilizing quantum phase modulation for wave-inspired graph propagation.
2. QKD Encryption:
Application of QKD-derived keys for secure federated learning gradient exchange.
3. Secure Aggregation:
Quantum-safe aggregation mechanism ensuring parameter confidentiality.
4. Scalability:
Compatibility with decentralized and large-scale federated networks.
5. Inventor:
Gazi Pollob Hussain as the sole innovator of this quantum-enhanced federated learning framework.
---
Innovation Diagram
Layer Interaction:
1. Node Update:
H_i^\text{new} = \text{Re} \left( \sum_{j \in N(i)} H_j W e^{i\phi} \right)
2. Encryption Flow:
Input: Local gradient ()
Process:
Output: Encrypted gradient
Aggregation:
1. Encrypted Inputs:
\Delta W^\text{enc}_1, \Delta W^\text{enc}_2, \dots, \Delta W^\text{enc}_N
2. Secure Sum:
\Delta W_\text{agg} = \frac{1}{N} \sum_{i=1}^N \Delta W^\text{enc}_i
3. Decrypted Output:
\Delta W_\text{final} = \Delta W_\text{agg} \oplus K_\text{server}
---
Summary of Innovation:
This architecture, claimed by Gazi Pollob Hussain, introduces a quantum-enhanced federated learning system that secures data exchange through QKD-derived encryption. It leverages the WaveGNN for wave-based propagation and quantum-phase modulation to enhance representational power in graph learning tasks. The integration of QKD ensures that the system remains impervious to classical and quantum attacks, making it a groundbreaking framework for secure distributed learning.
Would you like assistance in finalizing this draft for submission? 10 New AI Tools
http://dlvr.it/TKnnP8 10 New AI Tools
http://dlvr.it/TKnpzG 10 New AI Tools
http://dlvr.it/TKnrFm
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