Federated Training Checkpoint Migration Across Edge Servers
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Solution Overview
Problem
Existing federated learning systems face challenges in managing device mobility during training, leading to increased training times and reduced accuracy due to the need for restarting computations when devices move between edge servers.
Innovation Solution
A mechanism for migrating partially trained models from an origin edge server to a destination edge server, allowing computational devices to resume training without restarting, thereby reducing training time and maintaining model accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If devices move between edge servers during federated learning training, then device mobility is supported, but training time increases and accuracy decreases due to computation restart
Solution Approach 1:
The system performs preliminary actions by creating data checkpoints during the training process at the origin edge server. These checkpoints capture the state of model training at regular intervals, allowing the device to resume from the latest checkpoint rather than restarting from scratch when moving to a new edge server. This preliminary preparation of checkpoint data eliminates the need for full training restart.
Solution Approach 2:
The invention uses copying by transferring data checkpoints from the origin edge server to the destination edge server. Instead of transferring the entire training process or raw data, the system creates and copies checkpoint snapshots that represent the training state, enabling efficient resumption of training at the new server without repeating previous training work.
2Adaptability or versatility
If devices move between edge servers during federated learning training, then device mobility is supported, but model accuracy decreases due to computation restart
Solution Approach 1:
The system performs preliminary actions by creating data checkpoints during the training process at the origin edge server. These checkpoints capture the state of model training at regular intervals, allowing the device to resume from the latest checkpoint rather than restarting from scratch when moving to a new edge server. This preliminary preparation of checkpoint data eliminates the need for full training restart.
Solution Approach 2:
The invention uses copying by transferring data checkpoints from the origin edge server to the destination edge server. Instead of transferring the entire training process or raw data, the system creates and copies checkpoint snapshots that represent the training state, enabling efficient resumption of training at the new server without repeating previous training work.
3Reliability
If full training restart is performed when devices move between edge servers, then training consistency is maintained, but training time increases significantly
Solution Approach 1:
The system performs preliminary actions by creating data checkpoints during the training process at the origin edge server. These checkpoints capture the state of model training at regular intervals, allowing the device to resume from the latest checkpoint rather than restarting from scratch when moving to a new edge server. This preliminary preparation of checkpoint data eliminates the need for full training restart.
Solution Approach 2:
The invention uses copying by transferring data checkpoints from the origin edge server to the destination edge server. Instead of transferring the entire training process or raw data, the system creates and copies checkpoint snapshots that represent the training state, enabling efficient resumption of training at the new server without repeating previous training work.
4Loss of time
If data checkpoints are transferred between edge servers, then training resumption is enabled, but network overhead increases
Solution Approach 1:
The system extracts only the essential checkpoint data needed for training resumption rather than transferring complete training datasets or full model states. By taking out only the critical checkpoint information (model parameters, optimization state, and metadata), the system minimizes network transfer overhead while enabling effective training continuation.
Solution Approach 2:
The invention uses copying by transferring data checkpoints from the origin edge server to the destination edge server. Instead of transferring the entire training process or raw data, the system creates and copies checkpoint snapshots that represent the training state, enabling efficient resumption of training at the new server without repeating previous training work.
Data Source
AI summary
Cooperative training migration is performed by training, cooperatively with a computational device through a network, the neural network model, creating, during the iterations of training, a data checkpoint, the data checkpoint including the gradient values and the weight values of the server partition, the loss value, and an optimizer state, receiving, during the iterations of training, a migration notice, the migration notice including an identifier of a second edge server, and transferring, during the iterations of training, the data checkpoint to the second edge server.