Federated Learning Model Parameter Extraction for Privacy
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Solution Overview
Problem
Federated learning systems face challenges in maintaining the privacy of local training data, particularly in medical imaging applications, where sensitive data needs to be kept confidential and decentralized training methods struggle to preserve data privacy during model aggregation.
Innovation Solution
Implementing a method where edge devices train local neural networks using locally maintained data and share modified model updates with a central server, incorporating techniques like selective parameter sharing and noise addition to prevent data leakage, while aggregating models to form a global model without exposing sensitive training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If local training data is uploaded to central server for model aggregation, then model training effectiveness is improved, but data privacy and confidentiality are compromised
Solution Approach 1:
The patent extracts only the necessary model parameters (weights and biases) from the trained local models for aggregation, while leaving the sensitive training data localized at edge devices. This selective extraction enables central model improvement without requiring centralization of confidential data, thus resolving the contradiction between training effectiveness and data privacy.
Solution Approach 2:
The patent introduces an intermediary aggregation process that combines model parameters from multiple edge devices without directly accessing or reconstructing the underlying training data. This intermediary mechanism allows effective model aggregation while maintaining data confidentiality at the source, addressing the privacy-loss problem.
2Adaptability or versatility
If local models are shared with central server for aggregation, then collaborative learning capability is improved, but risk of data leakage increases
Solution Approach 1:
The patent extracts only the essential learning outcomes (model parameters) from local training processes for sharing, while retaining the sensitive training data locally. This extraction approach enables collaborative learning aggregation without exposing confidential data to potential leakage risks.
Solution Approach 2:
The patent creates and shares copies of model parameters rather than original training data. These parameter copies contain the learned knowledge necessary for collaborative learning while being mathematically transformed representations that cannot be reverse-engineered to reveal sensitive source data, thus enabling collaboration without data leakage.
3Object-affected harmful factors
If training data is decentralized at edge devices, then data privacy is maintained, but model aggregation efficiency decreases
Solution Approach 1:
The patent extracts compact model parameter representations from distributed local models, enabling efficient aggregation of decentralized learning results. This extraction transforms scattered local training outcomes into aggregateable form without requiring centralization of large volumes of raw data, thus maintaining privacy while improving aggregation efficiency.
Solution Approach 2:
Instead of aggregating raw training data from decentralized sources (the conventional inefficient approach), the patent inverts the process by aggregating pre-processed model parameters from edge devices. This inverted approach maintains data decentralization and privacy while dramatically improving aggregation efficiency by working with compact parameter representations rather than voluminous raw data.
Data Source
AI summary
Apparatuses, systems, and techniques to perform federated training of neural networks while maintaining control over dissemination of local models of neural networks from which aspects of local training data might be extracted. In at least one embodiment, a neural network is trained on local training data and a local model is provided to be aggregated with other local models into a global model that is in turn used for further local model training, wherein a provided local model or training is adjusted to reduce an ability to extract aspects of local training data therefrom.