Federated Learning Encoder Sharing for Privacy
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Solution Overview
Problem
In federated learning, the challenge lies in training machine learning models across multiple entities without sharing data, as data is spread across different entities and centers, making it difficult to combine and analyze, while ensuring privacy and confidentiality.
Innovation Solution
A system and method for federated machine learning that uses a registration manager, evaluator, and director to arrange participating entities in a collaborative relationship, measure performance, selectively identify and share encoders, and merge trained models to form a single shared model, ensuring data privacy by only sharing model parameters and updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is brought together to a single repository for training, then model training efficiency is improved, but data privacy and confidentiality are compromised
Solution Approach 1:
The system segments the training process into distributed local training at each entity and centralized model aggregation. Each entity trains locally on its own data using shared encoders, then shares only model parameters with the coordinator, which aggregates them into a global model. This segmentation allows efficient training while preserving data privacy.
Solution Approach 2:
The patent introduces an intermediary encoder layer that acts as a mediator between data and the machine learning model. The encoder processes input data into a standardized representation that can be universally understood across different entities, enabling collaboration without requiring direct data sharing between entities.
2Adaptability or versatility
If multiple encoders are used to handle different data types, then data processing capability is improved, but system complexity increases
Solution Approach 1:
The patent implements universal encoders that can process multiple types of input data (tabular, text, images) through a single unified architecture. The encoders are designed with multi-functional capabilities, allowing them to handle different data modalities without requiring separate specialized encoders for each type, thus reducing overall system complexity.
Solution Approach 2:
Instead of creating entirely separate encoder systems for different data types, the patent uses copying and adaptation strategies where a single encoder architecture can be instantiated multiple times with different parameters or configurations to handle different data types, reducing redundancy and complexity.
3Reliability
If data is shared across entities for collaborative training, then model performance is improved, but data security and confidentiality are compromised
Solution Approach 1:
The system extracts only the essential model parameters and updates from the full training process and shares these extracted components across entities. By taking out only the necessary information (model parameters rather than raw data), the system enables collaborative improvement of model performance while maintaining data security and confidentiality.
Solution Approach 2:
The encoder serves as an intermediary that processes data into a standardized format, allowing different entities to train models using their own data while maintaining compatibility. This intermediary layer enables model performance improvement through collaboration without compromising data security, as only the processed model parameters are shared, not the underlying data.
Data Source
AI summary
Embodiments relate to an input-encoding technique in conjunction with federation. Participating entities are arranged in a collaborative relationship. Each participating entity trains a machine learning model with an encoder on a training data set. The performance of each of the models is measured and at least one of the models is selectively identified based on the measured performance. An encoder of the selectively identified machine learning model is shared with each of the participating entities. The shared encoder is configured to be applied by the participating entities to train the first and second machine learning models, which are configured to be merged and shared in the federated learning environment.