Overview of Distributed Machine Learning Techniques for 6G Networks

Abstract

The main goal of this paper is to survey the influential research of distributed learning technologies playing a key role in the 6G world. Upcoming 6G technology is expected to create an intelligent, highly scalable, dynamic, and programable wireless communication network able to serve many heterogeneous wireless devices. Various machine learning (ML) techniques are expected to be deployed over the intelligent 6G wireless network that provide solutions to highly complex networking problems. In order to do this, various 6G nodes and devices are expected to generate tons of data through external sensors, and data analysis will be needed. With such massive and distributed data, and various innovations in computing hardware, distributed ML techniques are expected to play an important role in 6G. Though they have several advantages over the centralized ML techniques, implementing the distributed ML algorithms over resource-constrained wireless environments can be challenging. Therefore, it is important to select a proper ML algorithm based upon the characteristics of the wireless environment and the resource requirements of the learning process. In this work, we survey the recently introduced distributed ML techniques with their characteristics and possible benefits by focusing our attention on the most influential papers in the area. We finally give our perspective on the main challenges and advantages for telecommunication networks, along with the main scenarios that could eventuate.

Joint Air-Ground Distributed Federated Learning for Intelligent Transportation Systems

“…While considering the FL process over a vehicular system, this issue can even become more critical mainly due to dynamicity, presence of a large number of VTs, multiple server nodes, high sensitivity of vehicular data, and fatal impacts of a data breach, etc. Several techniques introduced over different IoT environments for enabling the secure and trustworthy FL process such as reputation management, Blockchain-based systems, data privacy-based perturbation techniques, secure aggregation techniques, secure multi-party computation, homomorphic encryption, back-door defenders, etc., need further analysis for creating a highly reliable FL over VNs [29], [30], [31].…”

Distributed Machine Learning and Native AI Enablers for End-to-End Resources Management in 6G

“…Finally, it should be considered that the methodological frameworks themselves are governed by parameters [72,73], such as frequency of communication between agents in FDC, the aggregation interval or the weighting of each agent's model in the FL-based frameworks, and the values for these parameters also need to be optimally selected. On a related note, additional improvements in FL-based frameworks can be made by introducing intermediate layers in a hierarchy towards aggregating models in more steps, such as in fog learning [74].…”

AI-Enabled Reliable QoS in Multi-RAT Wireless IoT Networks: Prospects, Challenges, and Future Directions

“…Some researchers have surveyed works on cross-layer design and optimization in wireless networks [7], [8], [9], [15] however, literature lacks a review of AI & ML applications and associated challenges in CLD/CLO. Similarly, many works have surveyed distributed network management [45], [46], [47] for improving traditional network parameters and did not address QoS provisioning at the edge through distributed learning. Different from the other works, we have focused on AI & ML applications in cross-layer design and optimization in multi-RAT IoT networks and distributed network management for the end objective of meeting IoT users/applications QoS and QoE.…”