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Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications.

Mohammed Aledhari¹, Rehma Razzak¹, Reza M Parizi¹

¹College of Computing and Software Engineering, Kennesaw State University, Marietta, GA, 30060 USA.

IEEE Access : Practical Innovations, Open Solutions

|October 1, 2020

Summary

Federated Learning (FL) enables collaborative model training across devices without sharing raw data. This approach enhances privacy and security, offering robust solutions for various industries.

Keywords:

Collaborative AI Decentralized Data Federated Learning Machine Learning On-Device AI Peer-to-peer network Privacy Security

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Federated Learning (FL) is a decentralized machine learning approach.
It trains algorithms across multiple devices or servers using local data samples.
This contrasts with traditional methods requiring data centralization.

Purpose of the Study:

To provide a comprehensive study of Federated Learning (FL).
To detail enabling software/hardware platforms, protocols, and real-life applications.
To offer a thorough summary of FL protocols, platforms, and use-cases for data scientists.

Main Methods:

Overview of FL concepts and technical details.
Exploration of FL enabling technologies, protocols, and applications.
Analysis of recent literature on FL challenges and related research.

Main Results:

FL generates robust models through collaborative training without data exchange.
It offers enhanced privacy, security, and data access privileges.
Detailed use-cases illustrate the integration of FL architectures and protocols.

Conclusions:

FL presents a privacy-preserving alternative to traditional machine learning.
Addressing industry-specific obstacles is crucial for widespread FL adoption.
This paper serves as a guide for building effective, privacy-preserving FL solutions.