Decentralized Federated Learning for Privacy and Scalability
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Solution Overview
Problem
Current AI systems face challenges in privacy protection and data communication load, and they are not adaptable to rapid environmental changes, leading to outdated AI models, especially in critical applications like medicine.
Innovation Solution
A decentralized federated learning system that allows agents to collect and train local machine learning models, with aggregators forming clusters to create semi-global models, reducing data transfer and enabling continuous adaptation and personalization while maintaining privacy through differential privacy techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is transferred to a centralized computation facility for AI model training, then model accuracy is improved, but privacy protection deteriorates and communication load increases
Solution Approach 1:
The patent extracts the training data from the centralized computation facility and keeps it locally at edge devices and client devices. Only model parameters and gradients are transmitted to the server, not the raw training data. This extraction principle resolves the contradiction by maintaining model accuracy through centralized aggregation while protecting privacy by removing sensitive data from the communication channel.
Solution Approach 2:
The patent introduces an intermediary mechanism where a central server aggregates model parameters and gradients from multiple clients without directly accessing their training data. The server acts as a mediator that coordinates the federated learning process, enabling centralized model improvement while preserving client-side data privacy through differential privacy techniques.
2Measurement precision
If data is transferred to a centralized computation facility, then model training quality is improved, but communication load increases
Solution Approach 1:
The patent extracts only the essential model parameters and gradient information from each client for transmission to the central server, rather than transferring entire datasets. This selective extraction significantly reduces communication load while maintaining model training quality through efficient parameter aggregation at the server.
3Measurement precision
If a centralized AI system is used, then initial model performance is improved, but adaptability to rapid environmental changes deteriorates
Solution Approach 1:
The patent implements a dynamic federated learning system where models are continuously updated at edge devices using local data streams. The system transitions from static centralized training to dynamic distributed learning, enabling rapid adaptation to environmental changes while maintaining strong initial performance through centralized model initialization and aggregation.
Solution Approach 2:
The patent establishes continuous learning cycles where models are constantly trained and updated at edge devices using real-time data, with periodic synchronization to the central server. This continuous useful action ensures the system adapts rapidly to environmental changes while maintaining high performance through ongoing model improvement.
4Object-affected harmful factors
If federated learning with multiple clients is implemented, then privacy protection is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal federated learning framework that handles multiple clients, devices, and data types through a standardized architecture. The central server and client devices follow common protocols for model exchange and aggregation, enabling privacy protection across diverse systems while managing complexity through unified multi-functional design patterns.
Data Source
AI summary
A system for decentralized federated learning is provided. The system comprises agents and aggregators coupled to a communication network. Each agent comprises a data collector collecting raw data; a memory storing the collected raw data and a local machine learning model; and a processor training the local machine learning model. Each aggregator comprises a model collector collecting the local machine learning models; a memory storing the collected local machine learning models; and a processor creating a cluster machine learning model from the local machine learning models. The aggregators communicate with each other and exchange the cluster machine learning models to create a semi-global machine learning model. Each of the aggregators sends the semi-global machine learning model to the associated agents. Each of the agents updates the local machine learning model with the semi-global machine learning model.