Federated Learning Selective Training for Resource Optimization
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Solution Overview
Problem
Federated Learning techniques consume significant resources (energy and processing) at client devices due to repeated training of machine learning models, especially when dealing with non-Identically Distributed data sets.
Innovation Solution
Client devices determine if training with local data sets will change the per-class performance of the model, only training when necessary to conserve resources, and utilize data distribution analysis and encryption techniques to maintain privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the machine learning model is trained repeatedly at client devices in each training round, then the global machine learning model achieves acceptable performance, but resource consumption (energy and processing) increases significantly
Solution Approach 1:
The system changes the parameter of training frequency from constant (every round) to variable (selective participation). Client devices evaluate their data distribution characteristics and per-class performance metrics to dynamically determine whether to participate in training, thereby adjusting the training parameter based on actual need rather than following a fixed schedule.
Solution Approach 2:
Client devices autonomously evaluate their own data characteristics and determine their own participation in training rounds. Each device independently assesses whether its local data distribution will meaningfully change per-class performance, and only engages in training when the evaluation indicates potential benefit, eliminating the need for centralized coordination of every training decision.
2Use of energy by moving object
If the machine learning model is trained selectively based on data distribution analysis, then resource consumption is reduced, but the complexity of determining training necessity increases
Solution Approach 1:
The training decision process is segmented into distinct evaluation steps: first analyzing data distribution characteristics, then comparing against per-class performance metrics, and finally determining participation based on predefined thresholds. This segmentation breaks down the complex decision-making into manageable, sequential operations that can be implemented efficiently at client devices.
Solution Approach 2:
The system performs preliminary analysis of data distribution and performance metrics before committing to training. By evaluating whether training will likely change per-class performance in advance, client devices can avoid unnecessary training computations, making the preliminary evaluation simpler than the full training process it prevents.
3Use of energy by moving object
If client devices maintain the machine learning model without training, then resource consumption is minimized, but the model may not adapt to local data characteristics
Solution Approach 1:
The system implements feedback through per-class performance metrics that inform training decisions. Client devices monitor how their local data distribution compares to overall performance requirements, and this feedback loop ensures training is triggered when adaptability benefits are expected, while maintaining resource efficiency when the model already performs well on local data characteristics.
Data Source
AI summary
Apparatus comprising means for: receiving a machine learning model, wherein the machine learning model is configured to classify input data into a plurality of classes; receiving a per-class performance of the machine learning model; obtaining a data distribution of a local data set; determining, based on the data distribution of the local data set, if training the machine learning model with the local data set will change the per-class performance of the machine learning model; and in response to determining that training the machine learning model with the local data set will change the per-class performance of the machine learning model: training the machine learning model using the local data set.