PATENT

@G|I|X 

Patent Submission Draft: Quantum-Enhanced Federated Learning with Quantum Key Distribution (QKD) --- 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?