Customized ANN Models Through Privacy-Preserving Federated Learning
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Solution Overview
Problem
Existing methods face challenges in providing customized artificial neural network (ANN) models to consumers without direct data export, particularly in scenarios where data privacy and regulatory restrictions prevent the transfer of training data, such as with medical data.
Innovation Solution
A federated learning approach where a central server transmits an ANN model to target data providers for training on their own data, receiving trained model parameters back, and generates a common model while evaluating provider contributions and determining compensation based on training data and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is exported directly for model training, then model customization is improved, but data privacy and security are compromised
Solution Approach 1:
A federated learning server is introduced as an intermediary between data providers and consumers. The server coordinates the training process, manages model iterations, and facilitates communication without allowing direct data access. This mediator enables customized model training while maintaining data privacy by keeping training data localized at each provider's end.
Solution Approach 2:
The training process is segmented into multiple iterations where different data providers contribute their local data and models independently. Each provider trains on their own segmented dataset, and only model parameters (not raw data) are shared with the federated learning server. This segmentation allows customization across multiple data sources while preserving individual data privacy.
2Reliability
If federated learning is used to protect data privacy, then data security is improved, but system complexity increases
Solution Approach 1:
The federated learning server performs multiple functions within a single system: it coordinates training iterations, manages model aggregation, handles provider registration, and facilitates secure communication. This multi-functional approach consolidates complexity into a centralized coordinator rather than requiring complex distributed coordination between all participants.
Solution Approach 2:
The system implements feedback loops where the federated learning server sends updated global models back to data providers, who then use these models for further local training. This iterative feedback mechanism automates the coordination process, reducing manual intervention and simplifying the overall system operation despite the distributed nature of learning.
3Adaptability or versatility
If multiple data providers are involved in federated learning, then data diversity is improved, but coordination overhead increases
Solution Approach 1:
The federated learning process operates in periodic iterations rather than continuously. In each iteration, the server collects models from participating providers, aggregates them, and distributes the updated global model. This periodic structure organizes coordination into discrete time slots, making the overhead manageable and predictable despite involving multiple diverse data providers.
Solution Approach 2:
The federated learning server merges models from multiple data providers into a single global model through aggregation. This combining operation consolidates the diverse training contributions into one unified model that can then be distributed back to all providers, reducing the coordination complexity by centralizing the integration process rather than requiring pairwise coordination between providers.
4Adaptability or versatility
If data providers are evaluated and compensated based on contribution, then fairness is improved, but evaluation complexity increases
Solution Approach 1:
The evaluation and compensation system replaces subjective manual assessment with automated computational metrics. The server objectively measures each provider's contribution based on quantifiable factors such as data quality, model improvement, and training efficiency. This automated evaluation system reduces complexity by using clear, measurable criteria rather than requiring complex judgment processes.
Data Source
AI summary
A data market using federated learning is disclosed. According to an embodiment, an operating method of a central server includes providing data provider information to a consumer, receiving, from the consumer, data request information including an artificial neural network (ANN) model, transmitting the data request information to target data providers, receiving, from the target data providers, a result of training of an individually trained ANN model, generating a common model based on the result of the training, and transmitting the common model to the consumer.