Mobile Body Selection for Federated Learning Efficiency
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Solution Overview
Problem
In distributed machine learning environments with heterogeneous computing nodes, existing systems face challenges in efficiently selecting suitable mobile bodies for machine learning tasks, often resulting in redundant data collection and processing due to nodes traveling in similar directions, which hampers efficient federated learning.
Innovation Solution
An information processing apparatus and method that acquire and utilize mobile body information, including position and travel direction data, to selectively choose nodes for machine learning, thereby avoiding redundant data collection and enhancing the efficiency of federated learning by selecting nodes traveling in different directions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If distributed machine learning is performed using heterogeneous computing nodes, then machine learning can be performed across multiple mobile bodies, but redundant data collection occurs when nodes travel in similar directions
Solution Approach 1:
The system performs preliminary acquisition of position information and travel direction information for each mobile body before selecting nodes for machine learning. This preliminary action enables the server to predict potential redundancy in data collection and proactively select nodes that will provide diverse, non-redundant data, thereby improving machine learning efficiency while minimizing redundant data collection.
2Quantity of substance
If nodes traveling in similar directions are selected for machine learning, then more computing nodes can participate, but the diversity of collected data decreases
Solution Approach 1:
The system applies local quality by selecting computing nodes based on their specific local characteristics - namely their travel directions and positions. Instead of treating all nodes uniformly, the server evaluates each node's unique trajectory and selects a subset where each selected node contributes data from a distinct local perspective (different travel direction), thereby maintaining data diversity while maximizing the number of participating nodes.
3Reliability
If position information is acquired for all mobile bodies, then suitable nodes can be selected, but information processing complexity increases
Solution Approach 1:
The system extracts only the essential information needed for node selection - specifically position information and travel direction information - from the complete set of mobile body data. By taking out only these critical parameters rather than processing all available information about each mobile body, the system achieves reliable node selection based on diversity while minimizing information processing complexity.
Data Source
Figure 1
Figure 2A~2B
Figure 3
AI summary
An information processing apparatus (3) includes a controller configured to: acquire mobile body information including information that indicates the respective positions of a plurality of mobile bodies (10) that are usable for machine learning; and select two or more mobile bodies (10) to be used for machine learning based on the mobile body information.