Federated Edge Learning : Design Issues and Challenges
arXiv:2009.00081
Abstract
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.
Submitted to IEEE Network Magazine
References in corpus (5)
- Asynchronous Federated Optimization
- Fair Resource Allocation in Federated Learning
- Federated Learning with Differential Privacy: Algorithms and Performance Analysis
- Energy-Efficient Radio Resource Allocation for Federated Edge Learning
- Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)