Genya Ishigaki

Title: Genya Ishigaki and Decentralized Federated Learning.

Introduction

Genya Ishigaki is an inventor associated with Richardson, Texas, and Tieset, Inc. His patent work addresses decentralized federated learning, an approach that enables machine-learning models to be developed across distributed agents while limiting the need to centralize raw data.

Latest Patents

Ishigaki is credited with 1 patent: “System and Method for Decentralized Federated Learning.” The invention describes agents that collect and store raw data and train local machine-learning models. Aggregators collect those local models and create cluster models, then exchange cluster models with one another to produce a semi-global model. The aggregators send that model back to their associated agents, which use it to update their local models.

Career Highlights

The patent reflects work on coordinating distributed learning across agents and aggregators. By exchanging model updates rather than relying solely on a central repository of raw data, the described system supports a decentralized process for combining learning across participating agents.

Collaborations

Ishigaki is associated with Tieset, Inc. His listed coworker is Kiyoshi Nakayama.

Conclusion

Genya Ishigaki’s patent focuses on a decentralized framework for combining local machine-learning models into broader shared models, highlighting his contribution to federated learning.