Fair Cost-Effective Device Selection in Distributed Learning
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Solution Overview
Problem
Existing federated learning methods fail to consider the fairness and variability of operational costs across participating clients, leading to cost-inequality scenarios where a few clients bear the majority of the costs.
Innovation Solution
A method for cost-effective and fair computing device selection in a distributed learning architecture, which involves calculating energy consumption operational costs based on dynamic electricity prices and end-device conditions, and performing multi-objective optimization to minimize cumulative operational costs and ensure fairness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If reputation-based techniques are used for client selection, then the server can efficiently select high-quality clients, but operational cost disparities increase causing a rich-gets-richer scenario
Solution Approach 1:
The patent implements a feedback mechanism where the server tracks and communicates operational cost information back to clients, and uses this information in subsequent selection decisions. The server monitors cumulative costs incurred by each client and adjusts selection probabilities to balance the distribution of costs, preventing the rich-gets-richer scenario while maintaining training efficiency.
2Measurement precision
If operational cost information is collected from clients, then fair cost distribution can be achieved, but communication overhead and privacy concerns increase
Solution Approach 1:
The patent extracts only the necessary operational cost information (such as energy consumption metrics) from clients rather than collecting comprehensive device data. This selective extraction approach achieves fair cost distribution by focusing on the specific parameters that influence operational costs, while minimizing communication overhead and preserving client privacy by not requiring transmission of raw device characteristics or training data.
3Measurement precision
If dynamic electricity prices and device conditions are considered, then operational costs are accurately calculated, but computational complexity increases
Solution Approach 1:
The patent performs preliminary calculations of operational costs using dynamic electricity prices and device conditions before the actual training process begins. By pre-computing cost estimates and using these as selection criteria, the system achieves accurate operational cost measurement without requiring complex real-time optimization computations during training rounds, thus reducing computational complexity while maintaining accuracy.
Data Source
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AI summary
A method, a server and computer programs for cost-effective and fair computing device selection in a distributed learning architecture are proposed. The method comprises executing a learning procedure between a server and a subset of selected computing devices until a stop criteria is reached. The subset is selected by the server: sending a request to a plurality of computing devices to determine their availability for a learning training round; checking whether static attributes of the available devices are accessible, or otherwise, sending a request to the available devices for their static attributes; sending a request to the available devices for dynamic attributes thereof; calculating, for each device, an energy consumption operational cost that will be incurred if the device participates in the learning training round using computation and communication power costs of each device; and performing, for each device, a multi-objective optimization that minimizes an overall cumulative operational cost of the device and fairness assessment.