Federated Model Mapping for Privacy-Preserving Multi-Client Learning
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Solution Overview
Problem
Machine learning systems face challenges due to the reliance on large amounts of data and the need for data representing all aspects of the problem, which can be hindered by insufficient data availability or differing data formats and encodings across clients, leading to privacy concerns and reduced model performance.
Innovation Solution
A federated learning approach where multiple machine learning models are trained locally on client data, using mapping rules to align data formats and apply models to mapped records, combining results without sharing raw data, and utilizing clusters for similar data quality, thereby enhancing model performance and privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple machine learning models are trained locally on client data, then model accuracy is improved by leveraging diverse data, but data sharing requirements increase which raises privacy concerns
Solution Approach 1:
The system segments the machine learning process into local model training at each client and centralized aggregation at the server. Each client trains models locally on its own data without sharing raw data, and only model parameters or results are shared with the federation server for aggregation. This segmentation resolves the contradiction by enabling diverse data utilization for improved accuracy while maintaining data privacy through local processing.
Solution Approach 2:
The system uses model parameter copies instead of original data copies. Clients train local models and share only the model parameters (weights, biases) with the federation server, never the actual training data. This allows the server to aggregate knowledge from multiple clients' diverse data while preserving privacy, as only abstracted model representations are transmitted.
2Adaptability or versatility
If data mapping rules are applied to align different data formats, then model applicability across clients is improved, but processing complexity increases
Solution Approach 1:
The system implements a universal data mapping framework that handles multiple data formats and encodings through standardized mapping rules. The federation server maintains a centralized mapping rule repository that can be applied uniformly across different clients' data formats. This universal approach enables models to be applied across diverse client data while managing complexity through centralized rule management rather than custom solutions for each client.
Solution Approach 2:
The system introduces data mapping rules as an intermediary layer between raw client data and machine learning models. Instead of directly processing diverse formats, the mapping rules transform various client data formats into a standardized intermediate representation that models can process. This intermediary layer simplifies the overall system by providing a uniform interface while handling format diversity.
3Object-affected harmful factors
If federated learning is implemented with multiple clients, then data privacy is protected by not sharing raw data, but network traffic and coordination overhead increase
Solution Approach 1:
The system extracts only the essential model parameters (weights and biases) from the complete machine learning process for transmission over the network. Clients perform all heavy computational work locally and extract only the necessary parameter updates to share with the federation server. This extraction principle protects privacy by never transmitting raw data while minimizing network traffic by sending only compact parameter representations rather than full datasets or complex model structures.
Data Source
AI summary
Some embodiments are directed to a federated machine learning, including the inference and training. Inference may be done by applying multiple machine learning models to a mapped record. The mapped record may be obtained by applying a mapping rule to a local record. The mapping rule may generalize or extend data features in the local record.