Adaptive Asynchronous Federated Learning Gradient Aggregation
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Solution Overview
Problem
Federated learning in edge computing systems often faces inefficiencies due to synchronous operations, where slower devices hinder the process, and data non-independence leads to model overfitting on faster devices, especially when data distributions across devices are highly non-identical.
Innovation Solution
Adaptive asynchronous federated learning method where clients pull global parameters, compute gradients, and push them to a server, which aggregates gradients with appropriate versions of the global parameter, selecting the best model based on validation dataset accuracy to avoid overfitting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If synchronous federated learning is used, then all devices are coordinated to update models together, but slower devices waste time and reduce overall system productivity
Solution Approach 1:
The system transitions from static synchronous updates to dynamic asynchronous updates, where each device can update the global model at its own pace without waiting for others. The server maintains a queue of pending updates and processes them dynamically, allowing fast devices to contribute more frequently while slow devices continue at their own speed, thereby improving overall productivity without time waste.
Solution Approach 2:
Devices perform model updates in advance and submit them to the server queue without waiting for synchronization points. The server accumulates these preliminary updates and applies them when appropriate, allowing devices to continue training independently and reducing idle time caused by waiting for slower participants.
2Productivity
If asynchronous updates are allowed without version matching, then faster devices can contribute more updates, but the model may fit data on fast devices only when data is highly non-IID
Solution Approach 1:
The server implements version checking feedback mechanisms where each incoming gradient is compared against the current global model version. If the gradient is from an outdated version, the server either discards it or adjusts it based on more recent updates. This feedback loop ensures that only relevant gradients are applied, maintaining model reliability across diverse data distributions while still allowing asynchronous updates from fast devices.
Solution Approach 2:
The system dynamically adjusts the aggregation strategy based on data distribution characteristics. When data is highly non-IID, the server applies more conservative aggregation with version matching to prevent overfitting to fast devices. When data is more homogeneous, it allows more aggressive asynchronous updates. This parameter change in aggregation behavior maintains reliability while maximizing productivity.
3Productivity
If gradients from outdated global parameter versions are aggregated, then more updates can be processed, but model convergence may be compromised
Solution Approach 1:
The server implements a selective gradient processing policy where gradients from significantly outdated versions are discarded to maintain convergence accuracy. However, gradients from moderately outdated versions are recovered and adjusted by combining them with more recent updates. This approach preserves useful information from fast devices while preventing convergence issues, balancing productivity with precision.
Data Source
AI summary
Techniques for adaptive asynchronous federated learning are described herein. An aspect includes providing a first version of a global parameter to a first client and a second client. Another aspect includes receiving, from the first client, a first gradient, wherein the first gradient was computed by the first client based on the first version of the global parameter and a respective first local dataset of the first client. Another aspect includes determining whether the first version of the global parameter matches a most recent version of the global parameter. Another aspect includes, based on determining that the first version of the global parameter does not match the most recent version of the global parameter, selecting a version of the global parameter. Another aspect includes aggregating the first gradient with the selected version of the global parameter to determine an updated version of the global parameter.