Federated Learning Approach for Lifetime Prediction of Semiconductor Lasers

TL;DR

This paper introduces a federated learning framework enabling laser manufacturers to collaboratively develop a laser lifetime prediction model without sharing sensitive data, achieving high accuracy and privacy preservation.

Contribution

The paper presents a novel federated learning approach specifically designed for laser lifetime prediction, enhancing privacy and model robustness.

Findings

Mean absolute error of 0.1 years achieved

Significant performance improvement over existing methods

Effective privacy-preserving collaborative modeling

Abstract

A new privacy-preserving federated learning framework allowing laser manufacturers to collaboratively build a robust ML-based laser lifetime prediction model, is proposed. It achieves a mean absolute error of 0.1 years and a significant performance improvement