Federated Multi-Task Model Training Progress Coordination
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Solution Overview
Problem
In federated multi-task learning scenarios, models for different tasks have varying training difficulties and durations, leading to unstable performance of the global model obtained through fusion.
Innovation Solution
A server manages the training progress of each model by using model measurement information to determine training strategies, such as stopping, restarting, or adjusting speeds, to synchronize the training progress of multiple models, ensuring a more stable global shared model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If models for different tasks are trained independently without coordination, then each model can be trained to its optimal performance, but the training durations and difficulties vary causing unstable global model performance
Solution Approach 1:
The server obtains model measurement information from clients and uses this feedback to determine training strategies for each model. Based on the measurement information, the server sends indication information to clients to adjust training speeds or stop/restart training, creating a closed-loop feedback mechanism that coordinates training progress across multiple models and stabilizes global model performance.
Solution Approach 2:
The training system dynamically adjusts training strategies based on real-time model measurement information. The server can change training speeds, stop or restart training for different models based on their actual performance status, making the training process adaptive and coordinated rather than static and independent.
2Reliability
If the server manages and coordinates training progress of multiple models, then global model performance stability improves, but system complexity increases
Solution Approach 1:
The server performs multiple functions: it obtains model measurement information, determines training strategies, sends indication information, and coordinates training progress for multiple models. By consolidating these management functions in the server, the system avoids the complexity of distributed coordination while achieving stable global model performance.
Data Source
AI summary
Example model training methods and apparatus are described. One example method includes that client devices measure locally trained models to obtain model measurement information, and report the model measurement information to a server. The server obtains, based on the model measurement information corresponding to the plurality of models, first indication information corresponding to each model, where the first indication information indicates a training strategy for a client device to train the model. The server sends the first indication information corresponding to each model to a client device that trains the model. In this way, the client devices adjust, based on the first indication information, the training strategies for training the models, to coordinate training progresses of the plurality of models.