Ranking Private Distributed Data Nodes and Models
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Solution Overview
Problem
Accessing and ranking private enterprise data and machine learning models across distributed resources is challenging due to privacy concerns and the lack of direct links between these resources, even with advancements in privacy-preserving technologies.
Innovation Solution
A method and system for ranking data nodes and model nodes by determining an edge between them based on influence in generating trained models and inference outputs, using a central server to receive and transfer weights, and calculating scores based on these edges, incorporating AI readiness and other factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is distributed across enterprise resources to maintain privacy, then user privacy is protected, but access and ranking of the data becomes difficult
Solution Approach 1:
The patent introduces a central server as an intermediary that coordinates distributed data nodes and model nodes. The server receives weights from data nodes, determines edges based on influence relationships, and calculates scores without requiring raw data to leave its premise. This mediator enables ranking functionality while preserving the distributed private nature of the data.
Solution Approach 2:
The patent replaces traditional mechanical linking systems (direct connections between data sources and users) with an influence-based scoring system. Instead of requiring direct access links, the system uses weight transfers and edge determinations based on model training relationships to establish indirect connections, enabling access to distributed data through computational relationships rather than physical connections.
2Reliability
If data nodes are not directly linked through the internet to maintain security, then privacy is preserved, but ranking and searching becomes challenging
Solution Approach 1:
The patent implements a feedback mechanism where data nodes transfer weights to the central server based on their influence in model training. The server uses these weights to determine edges and calculate scores, creating a feedback loop that enables ranking without direct links. The scoring system continuously refines rankings based on the accumulated weight information from distributed nodes.
Solution Approach 2:
The patent transforms the ranking problem from a direct link-based approach to a parameter-based approach. Instead of measuring connectivity through internet links, the system measures influence through weight parameters transferred during federated learning. The edge determination and scoring are based on these weight parameters rather than physical connection parameters.
3Reliability
If federated learning is used to train models on distributed data, then data privacy is maintained, but the complexity of managing distributed nodes increases
Solution Approach 1:
The patent merges the ranking functionality with the existing federated learning infrastructure. The same central server that coordinates model training also performs edge determination and score calculation. The weight transfers used for model improvement are simultaneously used for ranking purposes, combining multiple functions into a unified system that reduces overall complexity.
Solution Approach 2:
The central server is designed with multi-functionality, serving both as the coordination hub for federated learning and as the ranking system. It performs model training coordination, receives weight transfers, determines edges, and calculates scores all through a single unified platform, eliminating the need for separate ranking infrastructure and reducing system complexity.
Data Source
AI summary
A method for ranking includes the steps of receiving, at least one model from a central server to form at least one model node; training, the at least one model node with at least one data node to generate a trained model; generating, a weight from each of the at least one data node for the trained model; transferring, the weight from the at least one data node to the central server; inferencing, an inference output using the trained model and the data node; determining, an edge between the at least one data node and the model node, wherein the edge is determined depending on the influence of the data node or the model node on each other in generating the trained model and the inference output; determining a score for the data node and the model node based on the edge formed.