Work

Distributed Learning Methodologies for Massive Machine Type Communication

Year: 2024

Type: article

Abstract: Massive machine-type communication (MTC) addresses new IoT use cases such as connected vehicles, smart agriculture, and factory automation. However, major concerns face massive MTC applications, such as data privacy, latency constraints, and communication overhead. Distributed learning, an emerging technology that enables edge users to train various machine learning (ML) models without sharing raw data, is very promising for overcoming such concerns. However, distrib-uted learning still faces some difficulties related to the amount of training data, accuracy, complexity, and dynamic wireless environment. Herein, we elucidate different distributed learning methodologies, their limitations, and case studies in the spirit of massive MTC. Specifically, the article showcases the pros of Federated Learning (FL) in the context of dense MTC-based handwritten digits classification. After that, we highlight the differences between F1 and Federated Distillation (FD) and proceed to split, transfer, and meta-learning. Then, we discuss multi-agent reinforcement learning (MARL) in a UAV swarm empowered smart agriculture scenario. Finally, we present some challenges and future directions for distributed learning research in the context of massive MTC.

Source: IEEE Internet of Things Magazine

DOI: 10.1109/iotm.001.2400093

Authors: Matheus Valente da Silva, Eslam Eldeeb, Mohammad Shehab, Hirley Alves, Richard Demo Souza

Institutions: University of Oulu, King Abdullah University of Science and Technology, Universidade Federal de Santa Catarina

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,995