system
The system addresses the lack of benefits for clients in federated learning by evaluating their contributions and offering incentives, enhancing participation and engagement.
JP2026099127APending Publication Date: 2026-06-18TOYOTA JIDOSHA KK
Patent Information
- Authority / Receiving Office
- JP · JP
- Patent Type
- Applications
- Current Assignee / Owner
- TOYOTA JIDOSHA KK
- Filing Date
- 2024-12-06
- Publication Date
- 2026-06-18
AI Technical Summary
Technical Problem
Clients contributing to federated learning often do not receive benefits despite their contributions.
Method used
A system that integrates federated learning with an evaluation mechanism to assess client contributions and provides incentives based on these evaluations.
Benefits of technology
Provides benefits to clients by recognizing and rewarding their contributions to federated learning through incentives.
✦ Generated by Eureka AI based on patent content.
Smart Images
Figure 2026099127000001_ABST
Abstract
We reward clients who contribute to associative learning. [Solution] The system (FLS) comprises multiple clients (CLS1, CLS2) and a server (CS). The system performs federated learning in which each of the multiple clients learns a model using a training dataset, and the server repeatedly integrates the models using the learning results from the multiple clients. The system comprises an evaluation means (111) that evaluates the degree of contribution of the learning results of one of the multiple clients to the federated learning, and an incentive means (112) that provides an incentive to the one client according to the evaluation result by the evaluation means.
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