Asynchronous Federated Learning With Utility-Based Client Selection
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Solution Overview
Problem
Asynchronous federated learning often results in slower training speeds and lower model accuracy due to biased updates from faster clients and stale model updates from slow clients, exacerbating when data distributions are non-independent and identically distributed.
Innovation Solution
Select specific trainer clients with high utility (low loss) for asynchronous federated learning by blocking clients with low utility (high loss) to ensure balanced model training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If asynchronous federated learning is used to maximize client resource utilization, then productivity is improved, but training speed and model accuracy deteriorate due to biased updates from faster clients and stale updates from slow clients
Solution Approach 1:
The patent changes the selection parameter from random or synchronous to utility-based selection. By evaluating and selecting clients based on their measured utility (data quality metric), the system ensures that only clients with high-quality, representative data contribute to model updates, thereby maintaining high accuracy while preserving asynchronous operation efficiency
Solution Approach 2:
The patent implements a feedback mechanism where the server measures the utility of data from each client and uses this information to select appropriate clients for training. This feedback loop ensures that clients with stale or biased updates are excluded, preventing degradation of model accuracy while maintaining the asynchronous framework's productivity benefits
2Ease of operation
If all clients participate in asynchronous training independently, then ease of operation is improved, but model accuracy deteriorates due to noisy and stale model updates
Solution Approach 1:
The patent changes the participation criterion from universal participation to selective participation based on utility measurement. Clients continue to train independently, but only those with measured utility above a threshold are selected to contribute updates, filtering out noisy and stale updates that would harm model accuracy
Solution Approach 2:
The patent introduces an intermediary utility measurement and selection mechanism between independent client training and model aggregation. The server acts as a mediator that evaluates client utility and selectively accepts updates, reconciling the independence of asynchronous training with the need for high-quality model convergence
3Productivity
If faster clients are favored in asynchronous training, then productivity is improved, but manufacturing precision deteriorates due to biased updates that do not represent all clients
Solution Approach 1:
The patent changes the selection parameter from speed-based (implicit in asynchronous processing) to utility-based (explicit measurement of data quality). This ensures that training speed does not become the dominant factor, and model representativeness is maintained by selecting clients based on their data utility rather than their processing speed
Data Source
AI summary
In one embodiment, an illustrative method herein may comprise: training a machine learning model using asynchronous federated learning with a plurality of trainer clients and respective data on the plurality of trainer clients; determining a measured utility of the respective data on each of the plurality of trainer clients; and selecting specific trainer clients from among the plurality of trainer clients that have a corresponding measured utility greater than a given utility threshold to use for training the machine learning model using asynchronous federated learning.