Federated Learning: Opportunities and Challenges

By Priyanka Mary Mammen. arXiv.org, 2021

346 citations recorded by the source corpus.

Federated Learning (FL) is a concept first introduced by Google in 2016, in which multiple devices collaboratively learn a machine learning model without sharing their private data under the supervision of a central server. This offers ample opportunities in critical domains such as healthcare, finance etc, where it is risky to share private user information to other organisations or devices. While FL appears to be a promising Machine Learning (ML) technique to keep the local data private, it is also vulnerable to attacks like other ML models. Given the growing interest in the FL domain, this report discusses the opportunities and challenges in federated learning.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/012f7bbd7ce4eedb105d79c49388b49d0ae7c728

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. Mohammad R. Hayajneh
2. Haftay Gebreslasie Abreha
3. Mohamed Adel Serhani

Citations in the corpus

• Federated Learning in Edge Computing: A Systematic Survey (2022, background): “…and the motivation for FL X X [19] Tutorial on FL challenges X X X X [18] Overview of current research trends and relevant challenges X X X X [83] Discussed the opportunities and challenges in FL X X X X [84] described the existing FL and proposed an architecture of FL systems X X X X [17]…”