28 research outputs found
Convergence Time Optimization for Federated Learning over Wireless Networks
In this paper, the convergence time of federated learning (FL), when deployed
over a realistic wireless network, is studied. In particular, a wireless
network is considered in which wireless users transmit their local FL models
(trained using their locally collected data) to a base station (BS). The BS,
acting as a central controller, generates a global FL model using the received
local FL models and broadcasts it back to all users. Due to the limited number
of resource blocks (RBs) in a wireless network, only a subset of users can be
selected to transmit their local FL model parameters to the BS at each learning
step. Moreover, since each user has unique training data samples, the BS
prefers to include all local user FL models to generate a converged global FL
model. Hence, the FL performance and convergence time will be significantly
affected by the user selection scheme. Therefore, it is necessary to design an
appropriate user selection scheme that enables users of higher importance to be
selected more frequently. This joint learning, wireless resource allocation,
and user selection problem is formulated as an optimization problem whose goal
is to minimize the FL convergence time while optimizing the FL performance. To
solve this problem, a probabilistic user selection scheme is proposed such that
the BS is connected to the users whose local FL models have significant effects
on its global FL model with high probabilities. Given the user selection
policy, the uplink RB allocation can be determined. To further reduce the FL
convergence time, artificial neural networks (ANNs) are used to estimate the
local FL models of the users that are not allocated any RBs for local FL model
transmission at each given learning step, which enables the BS to enhance its
global FL model and improve the FL convergence speed and performance.Comment: This paper has been accepted in the IEEE Transactions on Wireless
Communication
Linear Regression over Networks with Communication Guarantees
A key functionality of emerging connected autonomous systems such as smart
cities, smart transportation systems, and the industrial Internet-of-Things, is
the ability to process and learn from data collected at different physical
locations. This is increasingly attracting attention under the terms of
distributed learning and federated learning. However, in connected autonomous
systems, data transfer takes place over communication networks with often
limited resources. This paper examines algorithms for communication-efficient
learning for linear regression tasks by exploiting the informativeness of the
data. The developed algorithms enable a tradeoff between communication and
learning with theoretical performance guarantees and efficient practical
implementations.Comment: Accepted at 3rd Annual Learning for Dynamics & Control Conference
(L4DC) 2021. arXiv admin note: substantial text overlap with arXiv:2101.1000
Optimization of User Selection and Bandwidth Allocation for Federated Learning in VLC/RF Systems
Limited radio frequency (RF) resources restrict the number of users that can
participate in federated learning (FL) thus affecting FL convergence speed and
performance. In this paper, we first introduce visible light communication
(VLC) as a supplement to RF in FL and build a hybrid VLC/RF communication
system, in which each indoor user can use both VLC and RF to transmit its FL
model parameters. Then, the problem of user selection and bandwidth allocation
is studied for FL implemented over a hybrid VLC/RF system aiming to optimize
the FL performance. The problem is first separated into two subproblems. The
first subproblem is a user selection problem with a given bandwidth allocation,
which is solved by a traversal algorithm. The second subproblem is a bandwidth
allocation problem with a given user selection, which is solved by a numerical
method. The final user selection and bandwidth allocation are obtained by
iteratively solving these two subproblems. Simulation results show that the
proposed FL algorithm that efficiently uses VLC and RF for FL model
transmission can improve the prediction accuracy by up to 10% compared with a
conventional FL system using only RF.Comment: WCNC202
6G White Paper on Machine Learning in Wireless Communication Networks
The focus of this white paper is on machine learning (ML) in wireless
communications. 6G wireless communication networks will be the backbone of the
digital transformation of societies by providing ubiquitous, reliable, and
near-instant wireless connectivity for humans and machines. Recent advances in
ML research has led enable a wide range of novel technologies such as
self-driving vehicles and voice assistants. Such innovation is possible as a
result of the availability of advanced ML models, large datasets, and high
computational power. On the other hand, the ever-increasing demand for
connectivity will require a lot of innovation in 6G wireless networks, and ML
tools will play a major role in solving problems in the wireless domain. In
this paper, we provide an overview of the vision of how ML will impact the
wireless communication systems. We first give an overview of the ML methods
that have the highest potential to be used in wireless networks. Then, we
discuss the problems that can be solved by using ML in various layers of the
network such as the physical layer, medium access layer, and application layer.
Zero-touch optimization of wireless networks using ML is another interesting
aspect that is discussed in this paper. Finally, at the end of each section,
important research questions that the section aims to answer are presented
Federated Learning for 6G: Applications, Challenges, and Opportunities
Traditional machine learning is centralized in the cloud (data centers).
Recently, the security concern and the availability of abundant data and
computation resources in wireless networks are pushing the deployment of
learning algorithms towards the network edge. This has led to the emergence of
a fast growing area, called federated learning (FL), which integrates two
originally decoupled areas: wireless communication and machine learning. In
this paper, we provide a comprehensive study on the applications of FL for
sixth generation (6G) wireless networks. First, we discuss the key requirements
in applying FL for wireless communications. Then, we focus on the motivating
application of FL for wireless communications. We identify the main problems,
challenges, and provide a comprehensive treatment of implementing FL techniques
for wireless communications
Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networks
This paper studies a federated learning (FL) system, where \textit{multiple}
FL services co-exist in a wireless network and share common wireless resources.
It fills the void of wireless resource allocation for multiple simultaneous FL
services in the existing literature. Our method designs a two-level resource
allocation framework comprising \emph{intra-service} resource allocation and
\emph{inter-service} resource allocation. The intra-service resource allocation
problem aims to minimize the length of FL rounds by optimizing the bandwidth
allocation among the clients of each FL service. Based on this, an
inter-service resource allocation problem is further considered, which
distributes bandwidth resources among multiple simultaneous FL services. We
consider both cooperative and selfish providers of the FL services. For
cooperative FL service providers, we design a distributed bandwidth allocation
algorithm to optimize the overall performance of multiple FL services,
meanwhile cater to the fairness among FL services and the privacy of clients.
For selfish FL service providers, a new auction scheme is designed with the FL
service owners as the bidders and the network provider as the auctioneer. The
designed auction scheme strikes a balance between the overall FL performance
and fairness. Our simulation results show that the proposed algorithms
outperform other benchmarks under various network conditions