mlsanityData to 14 Oct 2026

Federated Learning Approach for Lifetime Prediction of Semiconductor Lasers

arXiv 2203.12414 ↗First posted 19 Mar 2022eess.SP cs.LGcited by 2

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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

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

1 of 6 references.

References · 1

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669

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Cited by year

Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 1.

2 papers.

  1. 2022A Machine Learning-based Framework for Predictive Maintenance of Semiconductor Laser for Optical Communicationcited by 1
  2. 2025FedNET: Federated Learning for Proactive Traffic Management and Network Capacity Planningcited by 0