Edge Federated Learning Using Covariance Transfer and Blockchain
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Solution Overview
Problem
Existing AI deployment in edge computing environments face challenges with data privacy, communication overhead, and heterogeneity, leading to reduced model accuracy and reliability.
Innovation Solution
Integrate federated learning with blockchain and Kalman filter algorithms to enable decentralized model training, ensuring secure and efficient management of AI models across edge nodes, using smart contracts for validation and distribution, and adaptive model updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If federated learning is used for decentralized model training, then data privacy is improved, but communication overhead increases
Solution Approach 1:
The patent extracts only the essential model update parameters (covariance matrices) from the complete model training process, transmitting only these condensed representations between edge nodes and the blockchain network. This extraction approach maintains data privacy while significantly reducing communication overhead compared to transmitting full model updates or raw training data.
Solution Approach 2:
The patent transforms the model training state into a different parameter representation (covariance matrices) that captures the essential learning information in a compressed form. By changing the parameter representation from full model weights to covariance matrices, the system reduces communication requirements while preserving the benefits of federated learning for data privacy.
2Reliability
If blockchain technology is integrated for secure model management, then security and traceability are improved, but system complexity increases
Solution Approach 1:
The patent introduces smart contracts as intermediary components that automate and standardize the model training coordination, parameter validation, and update distribution processes across the blockchain network. These smart contracts serve as mediators that simplify interactions between edge nodes and the blockchain, reducing the operational complexity despite the underlying blockchain infrastructure.
3Loss of energy
If covariance matrix transfer is used for model updates, then communication efficiency is improved, but model accuracy may deteriorate
Solution Approach 1:
The patent implements feedback mechanisms where the blockchain network aggregates covariance matrices from multiple edge nodes and uses this aggregated information to guide subsequent training rounds. The feedback loop ensures that the condensed parameter transfers maintain or improve model accuracy by leveraging collective learning from all participants while keeping communication efficient.
Data Source
AI summary
This disclosure describes methods for adaptive machine learning in distributed edge computing. An edge node collects local data, selects a suitable large language model (LLM) or small learning model (SLM), trains it, and shares updates with a federated server or peer nodes. Another method matches AI functions with appropriate models, uses datasets with confidence values, and applies a Kalman Filter to assign weights and update covariance matrix confidence. In collaborative training, edge nodes store trained models with per-layer covariance values, transmit them to a control node, and update models based on aggregated inputs. Blockchain may be used for secure model storage and distribution, with smart contracts managing access and updates. These approaches support efficient, privacy-preserving learning by adapting models using statistical confidence and decentralized coordination.