Asynchronous Federated Learning with Split Models for Straggler Idle Time
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Solution Overview
Problem
Classic federated learning methods suffer from sub-optimal resource utilization, leading to idle time on servers and devices due to stragglers during local model training, which makes the process impractical.
Innovation Solution
The neural network model is split into a server model and a device model, with devices training auxiliary models independently, and the server aggregates parameters asynchronously, reducing idle time by training its model only when necessary and managing memory efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If classic federated learning waits for all devices to complete local model training before aggregation, then model convergence is ensured, but server and device idle time increases due to stragglers
Solution Approach 1:
The neural network model is segmented into device model (local) and server model (centralized), with different training schedules. Devices train their device models independently while the server trains the server model using activation sets from devices, allowing asynchronous progress without waiting for all devices to complete.
Solution Approach 2:
Devices perform preliminary actions by training their device models and generating activation sets, which are then used by the server to train the server model. This preliminary work enables the server to proceed with training independently rather than waiting for complete device model training cycles.
2Stability of the object's composition
If the server waits for device model training to complete before performing aggregation, then synchronization is maintained, but resource utilization decreases
Solution Approach 1:
The system transitions from static synchronous aggregation to dynamic asynchronous operation. The server dynamically trains the server model using available activation sets from devices at different completion times, while devices independently train their device models. Synchronization is maintained through the shared server model updates rather than rigid timing coordination.
3Productivity
If devices train independently without server coordination, then resource utilization improves, but model consistency deteriorates
Solution Approach 1:
The server model acts as an intermediary that coordinates between independently training devices. The server trains the server model using activation sets from multiple devices and periodically distributes updated server models to devices, ensuring model consistency is maintained while allowing devices to train independently at their own pace.
Data Source
AI summary
Federated learning with increased resource utilization is performed by performing computation iterations while maintaining an activation queue and a model queue. Each computation iteration includes: determining whether to perform aggregation, and then either adjusting, in response to determining to perform aggregation, parameters of the aggregated device model and the aggregated auxiliary model based on a first updated device model and corresponding first updated auxiliary model among the plurality of updated models in the model queue, or training, in response to not determining to perform aggregation, the server model based on a first activation set among the plurality of activation sets in the activation queue.