Hybrid Federated Learning for Faster Stable Model Convergence
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Solution Overview
Problem
Federated learning techniques such as federated averaging and federated distillation face issues of unstable and slow convergence, and overfitting due to client drift and data scarcity, respectively, when used individually for updating global machine learning models.
Innovation Solution
A hybrid federated learning technique that combines federated averaging and federated distillation, selecting client devices based on available data instances to perform multiple updates, using federated averaging for slower learning rate and federated distillation for faster convergence, thereby balancing convergence speed and overfitting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If federated averaging is used to update global machine learning models, then client drift is minimized, but convergence speed becomes slow and learning rate is reduced
Solution Approach 1:
The patent combines federated averaging and federated distillation into a hybrid approach. Federated averaging is used to maintain stability and control client drift, while federated distillation is used to accelerate convergence. The remote system receives both gradient updates (from federated averaging) and distilled knowledge (from federated distillation), merging these two mechanisms to resolve the contradiction between stability and speed.
2Speed
If federated distillation is used to update global machine learning models, then convergence speed increases, but overfitting occurs due to larger learning rate and data scarcity
Solution Approach 1:
The hybrid approach merges federated distillation (which provides fast convergence) with federated averaging (which provides regularization through gradient averaging). The gradient updates from federated averaging act as a regularizing force that prevents the model from overfitting to local client data, while the distillation process accelerates learning. This combination resolves the contradiction between speed and reliability.
3Stability of the object's composition
If federated averaging is used as the sole technique, then client drift is controlled, but computational efficiency is restricted by the least efficient client device
Solution Approach 1:
The patent segments the update process into two distinct mechanisms: gradient-based updates (federated averaging) and knowledge distillation updates (federated distillation). This segmentation allows different client devices to contribute differently - some clients can provide gradient updates while others provide distillation targets, thereby improving overall computational efficiency without compromising client drift control.
4Speed
If federated distillation is used as the sole technique, then convergence is faster than federated averaging, but the global model becomes almost identical to on-device models due to overfitting
Solution Approach 1:
The hybrid approach merges the benefits of both techniques by using federated distillation to accelerate convergence while simultaneously using federated averaging to maintain model diversity. The gradient averaging process ensures that the global model incorporates diverse information from all clients, preventing it from becoming too similar to any single on-device model, thus preserving information diversity while achieving fast convergence.
Data Source
AI summary
Implementations disclosed herein are directed to a hybrid federated learning (FL) technique that utilizes both federated averaging (FA) and federated distillation (FD) during a given round of FL of a given global machine learning (ML) model. Implementations may identify a population of client devices to participate in the given round of FL, determine a corresponding quantity of instances of client data available at each of the client devices that may be utilized during the given round of FL, and select different subsets of the client devices based on the corresponding quantity of instances of client data. Further, implementations may cause a first subset of the client devices to generate a corresponding FA update and a second subset of client devices to generate a corresponding FD update. Moreover, implementations may subsequently update the given global ML model based on the corresponding FA updates and the corresponding FD updates.