# Federated Learning Approach for Lifetime Prediction of Semiconductor Lasers

arXiv: https://arxiv.org/abs/2203.12414 · HTML: https://mlsanity.com/paper/2203.12414/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 6 references that are in the map.
- First posted on arXiv 19 Mar 2022.

## Builds on (most cited first, 1 of 1)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)

## Built on by (most cited first, 2 of 2)

- [A Machine Learning-based Framework for Predictive Maintenance of Semiconductor Laser for Optical Communication](https://mlsanity.com/paper/2211.02842/) (2022)
- [FedNET: Federated Learning for Proactive Traffic Management and Network Capacity Planning](https://mlsanity.com/paper/2511.06797/) (2025)

## Abstract (arXiv)

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