Federated Learning Coordinator Architecture for Secure Edge Collaboration
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Solution Overview
Problem
Current federated learning systems face challenges in enabling multiple enterprises to collaborate on edge devices securely and scalably, particularly when dealing with fragmented user data across many devices, which hinders cross-learning and effective user engagement strategies, and increases the risk of security and privacy breaches.
Innovation Solution
A system that employs coordinators to direct requests from requesters to relevant edge devices, utilizing vector search methods based on metadata and specialized coordinators for geographies or datasets, with agents orchestrating resource use and data quality evaluation, ensuring secure and scalable communication and collaboration among edge devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If federated learning systems enable multiple enterprises to collaborate on edge devices, then cross-learning capability and data volume for machine learning models are improved, but security and privacy risks increase due to the transfer of model weight updates
Solution Approach 1:
The patent introduces a blockchain-based intermediary layer that mediates between enterprises and edge devices. Smart contracts automatically verify and manage model weight updates, ensuring security and privacy protection while enabling cross-learning collaboration. The blockchain acts as a trusted mediator that prevents security risks without hindering productivity.
2Quantity of substance
If federated learning systems aggregate data from many edge devices, then data volume for machine learning models increases, but system complexity and coordination overhead increase
Solution Approach 1:
The patent segments the federated learning system into modular components: edge devices generate data, local aggregators process updates, blockchain nodes verify transactions, and central coordinators manage overall collaboration. This segmentation allows the system to handle large data volumes from many devices while keeping each component's complexity manageable through clear division of responsibilities.
3Productivity
If federated learning systems transfer model weight updates between enterprises, then cross-learning effectiveness improves, but the risk of information leakage increases
Solution Approach 1:
The patent implements a feedback mechanism where blockchain nodes continuously monitor and verify model weight updates through smart contracts. The system provides real-time feedback on the integrity and security of transferred information, allowing enterprises to collaborate effectively while preventing information leakage through automated verification and anomaly detection.
4Adaptability or versatility
If federated learning systems support a large number of edge devices, then data diversity and model accuracy improve, but communication overhead and coordination difficulty increase
Solution Approach 1:
The patent introduces a hierarchical dimension to the federated learning architecture, organizing edge devices into local groups with intermediate aggregators. This multi-dimensional structure allows the system to support a large number of diverse edge devices while reducing communication overhead by processing data locally before centralized coordination, effectively distributing the coordination burden across multiple levels.
Data Source
AI summary
A system to provide scalable and reliable communication mechanism between a plurality of requesters and a plurality of edge devices comprising one or more requests from said plurality requesters to one or more coordinators discovering one or more edge devices relevant to said request based on one or more search method and directing said requests to the one or more of said edge devices or to other coordinators, wherein the edge device comprises one or more data publishers providing data to an agent to execute said one or more request to create one or more responses and sending said one or more responses to the coordinators which are aggregating said one or more responses and sending to said one or more requesters for further processing.