A review of applications in federated learning

By Li Li, Yuxi Fan, Mike Tse, Kuo-Yi Lin. Computers & industrial engineering, 2020

1,458 citations recorded by the source corpus.

Abstract Federated Learning (FL) is a collaboratively decentralized privacy-preserving technology to overcome challenges of data silos and data sensibility. Exactly what research is carrying the research momentum forward is a question of interest to research communities as well as industrial engineering. This study reviews FL and explores the main evolution path for issues exist in FL development process to advance the understanding of FL. This study aims to review prevailing application in industrial engineering to guide for the future landing application. This study also identifies six research fronts to address FL literature and help advance our understanding of FL for future optimization. This study contributes to conclude application in industrial engineering and computer science and summarize a review of applications in FL.

Subfields: Artificial Intelligence, Computer Science Applications

View Paper: https://www.semanticscholar.org/paper/ea3751c6fa2410bf0ff7d34b0b42997cc2282509

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Yang Liu
2. Yan Kang
3. Yanhong Pu
4. Qian Yang
5. Yaqin Zhang
6. Xiaozhou Ye
7. Ye Ouyang
8. Tianyuan Zou

In Computer Science Applications

1. Yang Liu

Citations in the corpus

• Vertical Federated Learning: Concepts, Advances, and Challenges (2022, background): “Companies and institutions owning only small and fragmented data have constantly been looking for compensating data partners to collaboratively develop artificial intelligence (AI) technology for maximizing data utilization [7, 8].”
• A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics (2023, neutral): “The Federated Learning [374] is a promising technology to alleviate such a problem.”
• Federated Learning for Healthcare: Systematic Review and Architecture Proposal (2022, background): “[17] present a general overview of FL applications but does not provide an in-depth analysis of challenges related to medical data.”
• Federated Learning in Edge Computing: A Systematic Survey (2022, background): “…and caching schemes X X X X [72] Survey on MEC for the 5G network architecture X X X X X [80] Survey on the data privacy and security of FL X X X X [81] Survey on FL applications in industrial engineering and computer science X X X [61] Survey on FL research from five perspectives: data partition,…”