Co-distillation for Server-Federated Learning Model Convergence
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Solution Overview
Problem
Federated learning ML models are prone to catastrophic forgetting, where information learned from server data is forgotten when updating weights based on client-generated gradients, and existing techniques for combining server-based and decentralized learning are complex and require human intervention.
Innovation Solution
Co-distillation techniques that concurrently train client and server ML models with the same architecture, exchanging model weights to converge without requiring a fully trained teacher model, allowing for efficient and automated updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the global ML model is updated using federated learning with client-generated gradients, then the model adapts to client data and improves privacy protection, but the model suffers from catastrophic forgetting of information learned from server data
Solution Approach 1:
The server model is trained in advance on server data before the federated learning process begins. This preliminary training establishes a foundation of knowledge that the client models can later build upon without completely overwriting it, preventing catastrophic forgetting while still adapting to client-specific data distributions
Solution Approach 2:
The patent introduces an intermediate distillation process where the server model acts as a teacher model that periodically updates client models. This intermediary mechanism allows knowledge from server data to be transferred to client models without requiring direct retraining on server data, thus preventing information loss while maintaining adaptability
2Reliability
If traditional federated learning is used with separate server and client training phases, then privacy is protected, but the training process becomes complex and requires human intervention
Solution Approach 1:
The patent merges the server-based training and federated learning processes into a unified co-distillation framework. Instead of separate phases requiring manual intervention, the system continuously exchanges gradients and model updates between server and clients in an automated manner, reducing complexity while maintaining privacy protection through the same decentralized gradient transmission mechanism
3Measurement precision
If the global ML model is initially trained on server data and then fine-tuned with client data, then the model achieves good initial performance, but prediction churn increases during updates
Solution Approach 1:
The system implements continuous feedback loops where client models send gradients back to the server model, which then updates and transmits refined weights back to clients. This iterative feedback mechanism allows the model to gradually adapt to client data while maintaining stability, as each update is informed by aggregated feedback from multiple clients rather than abrupt changes
Data Source
AI summary
A method includes training a client machine learning (ML) model on client training data at a client device. While training the client ML model, the method also includes obtaining, from a server, server model weights of a server ML model trained on server training data, the server training data different that the client training data. While training the client ML model, the method also includes: transmitting, to the server, client model weights of the client ML model; updating the client ML model using the server model weights; obtaining, from the server, updated server model weights of the server ML model, the updated server model weights updated based on the transmitted client model weights; and further updating the client ML model using the updated server model weights.