Decentralized Federated Learning via Random Walk Graph

The patent extracts and removes embeddings from the federated learning process entirely. Instead of sharing embeddings between client devices, the system trains models independently on local data without extracting or transmitting embedding representations, thereby eliminating the vector space where sensitive information could be exposed while still achieving diverse data utilization through distributed training

The patent segments the federated learning process into completely isolated client-side training operations. Each client device trains its model independently on its own data without any sharing of model parameters, embeddings, or intermediate representations, creating segmented isolation that prevents sensitive data exposure while maintaining training diversity

The patent implements self-service decentralized federated learning where each client device independently performs model training, evaluation, and decision-making without requiring complex coordination protocols, centralized servers, or intricate communication mechanisms. The system achieves enhanced security and privacy through this simple, autonomous approach where devices serve themselves without external intervention

Instead of the conventional approach where a central server coordinates federated learning and clients report back, the patent inverts the architecture to complete decentralization where no central coordination exists. This inversion simplifies the system by eliminating the need for complex server-client communication protocols, aggregation mechanisms, and centralized control, achieving security through architectural simplicity rather than complex protective measures