Federated Neural Model Updates Using Teacher-Student Labeling
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Solution Overview
Problem
Existing machine learning models, particularly those deployed on decentralized devices, face challenges in updating their performance due to resource constraints and lack of access to diverse datasets, limiting their ability to adapt to evolving language and improve accuracy.
Innovation Solution
A system combining federated learning and self-supervised learning techniques, utilizing a 'teacher' model to generate labeled datasets for 'student' models on decentralized devices, enabling continuous learning without persisting raw data or gradients, thus optimizing resource usage and improving model performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are deployed on decentralized devices, then user privacy is protected and data security is improved, but model update capability deteriorates due to resource constraints and lack of access to diverse datasets
Solution Approach 1:
A server acts as an intermediary that coordinates federated learning processes across decentralized devices. The server aggregates model updates from multiple devices, manages the learning cycles, and distributes improved model versions back to devices, enabling collective learning while preserving individual device privacy and resource constraints
Solution Approach 2:
Each decentralized device performs local model training using its own data without needing to share raw data with other devices or central servers. The devices independently compute model updates and contribute only the necessary gradient information, allowing self-contained learning that respects privacy constraints while still improving models through collective experience
2Measurement precision
If traditional supervised learning is used for model updates, then model accuracy can be improved, but resource consumption increases due to the need for large labeled datasets and extensive computing power
Solution Approach 1:
The system employs self-supervised learning where the model generates its own training labels from unlabeled data through pretext tasks or predictive objectives. This eliminates the need for expensive manual annotation while still providing sufficient signal for effective learning, allowing devices to improve model accuracy using only their local computing resources and existing data
Solution Approach 2:
Instead of requiring complete labeled datasets for effective learning, the system uses partial supervision where only a portion of the learning signal comes from labeled data while the remainder comes from self-supervised signals. This reduces the burden of data annotation and computing resource requirements while maintaining model accuracy
Data Source
AI summary
A first neural network (NN) model may generate labels for training a second NN model. The second NN model may represent instances of a NN model operating on multiple different devices (e.g., decentralized user and/or edge devices). The system may include using a “teacher” model to process data received by one or more of the devices to generate a labeled dataset. The system may use the labeled dataset and a “student” model to calculate gradient data for updating the student model. The student model may be the same or similar to NN model instances operating on the devices. The system may validate the updated student model to determine, for example, whether it exhibits improved performance when processing the newly received data and/or historical data. The system may distribute the validated update to the devices.