Federated Learning: Opportunities and Challenges

Priyanka Mary Mammen

Federated Learning: Opportunities and Challenges

Machine Learning 2021-01-15 v1 Distributed, Parallel, and Cluster Computing

Authors: Priyanka Mary Mammen

Abstract

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.

Keywords

federated learning federated graph learning

Cite

@article{arxiv.2101.05428,
  title  = {Federated Learning: Opportunities and Challenges},
  author = {Priyanka Mary Mammen},
  journal= {arXiv preprint arXiv:2101.05428},
  year   = {2021}
}

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