2,541 research outputs found
Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
We envision a mobile edge computing (MEC) framework for machine learning (ML)
technologies, which leverages distributed client data and computation resources
for training high-performance ML models while preserving client privacy. Toward
this future goal, this work aims to extend Federated Learning (FL), a
decentralized learning framework that enables privacy-preserving training of
models, to work with heterogeneous clients in a practical cellular network. The
FL protocol iteratively asks random clients to download a trainable model from
a server, update it with own data, and upload the updated model to the server,
while asking the server to aggregate multiple client updates to further improve
the model. While clients in this protocol are free from disclosing own private
data, the overall training process can become inefficient when some clients are
with limited computational resources (i.e. requiring longer update time) or
under poor wireless channel conditions (longer upload time). Our new FL
protocol, which we refer to as FedCS, mitigates this problem and performs FL
efficiently while actively managing clients based on their resource conditions.
Specifically, FedCS solves a client selection problem with resource
constraints, which allows the server to aggregate as many client updates as
possible and to accelerate performance improvement in ML models. We conducted
an experimental evaluation using publicly-available large-scale image datasets
to train deep neural networks on MEC environment simulations. The experimental
results show that FedCS is able to complete its training process in a
significantly shorter time compared to the original FL protocol
A survey of self organisation in future cellular networks
This article surveys the literature over the period of the last decade on the emerging field of self organisation as applied to wireless cellular communication networks. Self organisation has been extensively studied and applied in adhoc networks, wireless sensor networks and autonomic computer networks; however in the context of wireless cellular networks, this is the first attempt to put in perspective the various efforts in form of a tutorial/survey. We provide a comprehensive survey of the existing literature, projects and standards in self organising cellular networks. Additionally, we also aim to present a clear understanding of this active research area, identifying a clear taxonomy and guidelines for design of self organising mechanisms. We compare strength and weakness of existing solutions and highlight the key research areas for further development. This paper serves as a guide and a starting point for anyone willing to delve into research on self organisation in wireless cellular communication networks
Self-Evolving Integrated Vertical Heterogeneous Networks
6G and beyond networks tend towards fully intelligent and adaptive design in
order to provide better operational agility in maintaining universal wireless
access and supporting a wide range of services and use cases while dealing with
network complexity efficiently. Such enhanced network agility will require
developing a self-evolving capability in designing both the network
architecture and resource management to intelligently utilize resources, reduce
operational costs, and achieve the coveted quality of service (QoS). To enable
this capability, the necessity of considering an integrated vertical
heterogeneous network (VHetNet) architecture appears to be inevitable due to
its high inherent agility. Moreover, employing an intelligent framework is
another crucial requirement for self-evolving networks to deal with real-time
network optimization problems. Hence, in this work, to provide a better insight
on network architecture design in support of self-evolving networks, we
highlight the merits of integrated VHetNet architecture while proposing an
intelligent framework for self-evolving integrated vertical heterogeneous
networks (SEI-VHetNets). The impact of the challenges associated with
SEI-VHetNet architecture, on network management is also studied considering a
generalized network model. Furthermore, the current literature on network
management of integrated VHetNets along with the recent advancements in
artificial intelligence (AI)/machine learning (ML) solutions are discussed.
Accordingly, the core challenges of integrating AI/ML in SEI-VHetNets are
identified. Finally, the potential future research directions for advancing the
autonomous and self-evolving capabilities of SEI-VHetNets are discussed.Comment: 25 pages, 5 figures, 2 table
- …