Federated Edge Learning : Design Issues and Challenges

08/31/2020
by   Afaf Taïk, et al.
0

Federated Learning (FL) is a distributed machine learning technique, where each device contributes to the learning model by independently computing the gradient based on its local training data. It has recently become a hot research topic, as it promises several benefits related to data privacy and scalability. However, implementing FL at the network edge is challenging due to system and data heterogeneity and resources constraints. In this article, we examine the existing challenges and trade-offs in Federated Edge Learning (FEEL). The design of FEEL algorithms for resources-efficient learning raises several challenges. These challenges are essentially related to the multidisciplinary nature of the problem. As the data is the key component of the learning, this article advocates a new set of considerations for data characteristics in wireless scheduling algorithms in FEEL. Hence, we propose a general framework for the data-aware scheduling as a guideline for future research directions. We also discuss the main axes and requirements for data evaluation and some exploitable techniques and metrics.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
03/26/2023

A Generalized Look at Federated Learning: Survey and Perspectives

Federated learning (FL) refers to a distributed machine learning framewo...
research
04/29/2021

From Distributed Machine Learning to Federated Learning: A Survey

In recent years, data and computing resources are typically distributed ...
research
08/23/2023

A Survey for Federated Learning Evaluations: Goals and Measures

Evaluation is a systematic approach to assessing how well a system achie...
research
07/03/2023

Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directions

The development of applications based on artificial intelligence and imp...
research
07/20/2023

Heterogeneous Federated Learning: State-of-the-art and Research Challenges

Federated learning (FL) has drawn increasing attention owing to its pote...
research
12/02/2020

Distributed Machine Learning for Wireless Communication Networks: Techniques, Architectures, and Applications

Distributed machine learning (DML) techniques, such as federated learnin...

Please sign up or login with your details

Forgot password? Click here to reset