Federated Learning Loop Network for Privacy-Preserving Model Training
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Solution Overview
Problem
The centralization of data for machine learning poses challenges in balancing predictive performance with data privacy and confidentiality, as sensitive data needs to be protected from unauthorized access and misuse, while also ensuring transparency and user alignment, which is difficult to achieve in current centralized methodologies.
Innovation Solution
A federated learning network is created using a master node that connects worker nodes with a central aggregator without exposing their IP addresses, allowing model training and evaluation while maintaining data privacy through loop networks that shield worker nodes from direct communication with central aggregators, using a master node to manage and monitor the network, update software, and communicate information securely.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is centralized in a single database for machine learning training, then predictive performance is improved, but data privacy and confidentiality are compromised
Solution Approach 1:
The system segments the centralized data architecture into distributed worker nodes, each holding local data subsets. This segmentation allows multiple data sources to contribute to model training without centralizing the actual data, thus improving predictive performance while maintaining data privacy and confidentiality at each node.
Solution Approach 2:
The patent introduces a central aggregator as an intermediary that coordinates model training across distributed worker nodes. The aggregator collects and aggregates model updates from workers without accessing the underlying sensitive data, enabling collaborative learning while preserving data confidentiality through this intermediary layer.
2Productivity
If federated learning is implemented with direct communication between central aggregator and worker nodes, then model training efficiency is improved, but network security and data protection are weakened
Solution Approach 1:
The patent introduces loop networks as intermediary communication pathways between the central aggregator and worker nodes. These loop networks enable model training coordination while shielding direct IP address exposure, thus maintaining training efficiency while reducing information leakage risks through the intermediary network structure.
Solution Approach 2:
The system uses model copies and parameters rather than direct data sharing between nodes. Worker nodes receive model copies from the aggregator, perform local training, and return updated model parameters without exposing their underlying data or direct network identities, thus maintaining efficiency while protecting against information leakage.
Data Source
AI summary
A method and apparatus of a device that trains or evaluates a model is described. In an exemplary embodiment, the device creates a loop network between a central aggregating node and a set of one or more worker nodes, where the loop network communicatively couples the central aggregating node. The device further receives and broadcasts a model training or evaluation request from one of the nodes in the loop network to one or more other nodes in the loop network.