Research Article
A Review of Federated Learning: Algorithms, Frameworks and Applications
@INPROCEEDINGS{10.1007/978-3-031-34896-9_20, author={Lutho Ntantiso and Antoine Bagula and Olasupo Ajayi and Ferdinand Kahenga-Ngongo}, title={A Review of Federated Learning: Algorithms, Frameworks and Applications}, proceedings={Towards new e-Infrastructure and e-Services for Developing Countries. 14th EAI International Conference, AFRICOMM 2022, Zanzibar, Tanzania, December 5-7, 2022, Proceedings}, proceedings_a={AFRICOMM}, year={2023}, month={6}, keywords={Federated Learning Machine Learning Federate Averaging}, doi={10.1007/978-3-031-34896-9_20} }- Lutho Ntantiso
Antoine Bagula
Olasupo Ajayi
Ferdinand Kahenga-Ngongo
Year: 2023
A Review of Federated Learning: Algorithms, Frameworks and Applications
AFRICOMM
Springer
DOI: 10.1007/978-3-031-34896-9_20
Abstract
In today’s world, artificial intelligence (AI) and machine learning (ML) are being widely adopted at an exponential rate. A key requirement of AI and ML models is data, which often must be in proximity to these models. However, it is not always possible to “bring data to the model”, due to several reasons including legal jurisdictions, or ethical reasons, hence, a “taking the model to the data” might be a viable alternative. This process is called Federate Learning (FL), and it is a ML technique that allows devices or clients to collaboratively learn a shared model from the central server, while keeping the training data local and isolated. This ensures privacy and bandwidth preservation, especially in resource constrained environments. In this paper, a review of FL is done with a view of presenting the aggregation models, frameworks, and application areas, as well as identifying open challenges/gaps for potential research works.
