# Mobile Augmented Reality with Federated Learning in the Metaverse

Xinyu Zhou, Jun Zhao The authors are all with Nanyang Technological
University, Singapore. Emails<EMAIL_ADDRESS><EMAIL_ADDRESS>
###### Abstract

The Metaverse is deemed the next evolution of the Internet and has received
much attention recently. Metaverse applications via mobile augmented reality
(MAR) require rapid and accurate object detection to mix digital data with the
real world. As mobile devices evolve, they become more potent in computing.
Hence, their computational resources can be leveraged to train machine
learning models. In light of the increasing concerns of user privacy and data
security, federated learning (FL) has become a promising distributed learning
framework for privacy-preserving analytics. In this article, FL and MAR are
brought together in the Metaverse. We discuss the necessity and rationality of
the combination of FL and MAR. The prospective technologies that power FL and
MAR in the Metaverse are also identified. In addition, existing challenges
that prevent the fulfilment of FL and MAR in the Metaverse and several
application scenarios are presented. Finally, two case studies of Metaverse
FL-MAR systems are demonstrated.

###### Index Terms:

Metaverse, augmented reality, virtual reality, federated learning.

##  I Introduction

The Metaverse has become a hot topic recently. Mark Zuckerberg made the term
famous in 2021 when he announced that Facebook would change its name to Meta
and shift its future to build Metaverse technologies. The Metaverse integrates
augmented reality (AR), virtual reality (VR) and 3D technologies to create a
fully immersive virtual world. Mobile augmented reality (MAR) brings the
Metaverse to mobile user equipments. With the development of mobile
technologies, it has been increasingly common for mobile users to leverage AR
services to interact and entertain themselves in the real world. As machine
learning technologies are applied to mobile devices, people are developing
more intelligent MAR applications in the Metaverse scenarios such as daily
communications, entertainment, medical care, travel, transportation, etc.

Fig. 1 depicts a scenario where users can see descriptions of each building.
Such MAR applications require rapid and accurate object detection to mix
digital data with the real world. With the fast development of wearable and
mobile devices (e.g., Google Glass, Microsoft Hololens), such a scenario is
not a figment of the imagination. Researchers have implemented effective
object detection algorithms on mobile devices [1].

Usually, current machine learning models require large amounts of data for
training to achieve good performance, whereas it is a challenging task to
collect those data. As concerns about data security and privacy increase,
related regulations and laws are being introduced one after another. Hence,
considering the growing difficulty of collecting sensitive datasets to a
server for centralized training, distributed learning is a big trend in the
future. In an MAR system, devices can train their object detection models
separately. However, a mobile device can only store or collect limited data,
resulting in a less accurate model. To address this, federated learning (FL)
can be incorporated into the MAR system. FL was proposed by Google in 2017
[2]. It allows each device to train a shared model collaboratively without
sharing local data with others. As shown in Fig. 1, after a few local
iterations, each device uploads its local model parameter to a central base
station, and the station will send back an updated global model to each device
for continuous training. We refer to the system as the FL-MAR system.

Figure 1: The general system model of mobile augmented reality (MAR) with
federated learning (FL) in the Metaverse.

Integrating FL into the design of the MAR system poses some challenges. First,
limited communication and computing resources can lead to latency between the
server and users. Second, achieving a satisfactory model often requires many
local iterations and communication times, which also adds a large amount of
energy consumption for mobile devices. Moreover, latency and energy
consumption are often in conflict. It is necessary to find an appropriate
resource allocation strategy to optimize latency and energy consumption.
Besides, in the FL-MAR system, the video frame resolution affects the object
recognition accuracy, and it influences the computation energy and time when
training on each device. As a result, to minimize latency and energy
consumption and maximize the detection accuracy, we should find how to assign
communication and computation resources (i.e., transmission power, bandwidth,
CPU frequency and video frame resolution) for each device.

This article first discusses promising technologies for FL-MAR in the
Metaverse in Section II. Then, in Section III, we present existing challenges
for the applications. Section IV demonstrates various application scenarios of
FL-MAR in the Metaverse. Finally, Section V eloaborates two case studies for
optimizing resource allocation of FL-MAR systems in the Metaverse. The
framework of this article is illustrated in Fig. 2.

Figure 2: The framework of this article.

The contributions are as follows:

  * •

Incorporating FL and MAR into the Metaverse is presented. For the proposed
incorporation, we further discuss prospective scenarios.

  * •

Challenges in the applications of FL-MAR to the Metaverse are also listed from
different aspects, including limited communication/communication resources,
security and privacy, etc.

  * •

To demonstrate the practicality, we demonstrate two case studies of FL-MAR
systems in the Metaverse. One is FDMA-enabled, and the other is based on NOMA.

##  II Enabling Technologies for FL-MAR in the Metaverse

This section lists technologies needed for applying FL and MAR to the
Metaverse.

###  II-A Channel access methods

Frequency Division Multiple Access (FDMA). FDMA is a channelization protocol
that divides the frequency band into non-overlapping channels of equal
bandwidth. Besides, each channel is assigned to one user only for the
conversation period. FDMA is one of the most commonly used analog multiple
access methods. It has some advantages: 1) FDMA systems are technically easy
to be implemented. 2) Signals can be transmitted simultaneously while not
interfering with each other. 3) The capacity can be increased by decreasing
the information bitrate and leveraging efficient numerical codes. Moreover,
FDMA also has some disadvantages: 1) Bandwidth utilization is limited since
channels will be idle if users do not utilize them. 2) If many signals of
different frequencies are transmitted simultaneously, inter-modulation
distortion is possible to happen at the transponder.

Time Division Multiple Access (TDMA). TDMA allows different users to share the
same frequency by dividing each channel into different time intervals. At each
time interval, the frequency is used for one user exclusively. Compared to
FDMA, the advantages of TDMA are: 1) The transmission rate is flexible because
multiple slots can be allocated to one user. 2) It can handle the changeable
bit rate. However, the disadvantages include the implementation complexity and
the requirement of synchronization.

Non-orthogonal Multiple Access (NOMA). NOMA has been seen as a promising
technology for intensifying the throughput in future wireless systems. Unlike
conventional orthogonal multiple access (OMA), it enables multiple users on
the same channel to be multiplexed to maximize the throughput and lower the
latency of the system. It adopts superposition coding at the transmitter and
utilizes successive interference cancellation (SIC) at the receiver to
distinguish signals of users. Hence, it increases OMA’s rate region.

For other channel access schemes, such as CDMA, SDMA and OFDMA, interested
readers can refer to [3].

###  II-B Semantic Communication

Semantic communication has been deemed the breakthrough of Shannon’s paradigm
as it transmits only the relevant semantic information about the specific
resource. It does not aim at the accurate transmission of bit sequences. For
example, when transmitting a figure, semantic communications will extract the
relevant features of the figure for transmission by using semantic encoding
[4]. Hence, the data traffic can be significantly lowered, which is why it can
be one of the promising solutions leading to an efficient communication
network in the Metaverse. Studies of semantic communication for the Metaverse
are in the early stage [5]. Since VR/AR applications in the Metaverse require
a seamless experience, the requirements of low latency and high-speed
transmission may be satisfied by semantic communications.

###  II-C Over-the-Air Computation

Over-the-air computation (AirComp) enables computation function by adding the
analog wave in a multiple-access channel. By utilizing the interference for
implementing the computation function, the wireless channel can be used as a
computer. In a distributed system, the signals sent by mobile devices are
superposed over the air and aggregated by the receiver as a weighted sum. The
weights represent channel coefficients [6].

In the Metaverse, numerous devices are connected to communicate through the
communication network. Large amounts of data are transmitted by various
devices simultaneously while devices wait for immediate feedback. The advent
of over-the-air computation may help to build low-latency communication
networks in the Metaverse.

###  II-D Mobile Edge Computing

Before the emergence of edge computing, cloud computing was the new paradigm
of computing at that time. Cloud computing refers to computing, network
control and storage in the clouds. Generally, clouds are servers (e.g., data
centers) that can be approached through the Internet. However, such servers
are usually located far away from user devices, which results in long latency.
In 2014, European Telecommunications Standard Institute (ETSI) proposed the
concept of mobile edge computing (MEC). In the IT service environment, MEC
equips cloud-computing capabilities at the edge of mobile networks in the
vicinity of mobile users. It aims at reducing latency and providing high-
efficient network operations and services [5].

MEC is deployed at access points, such as small base stations, edge servers,
users’ computers, etc. FL-MAR systems consist of massive mobile devices.
Hence, if utilizing the idle computing resources of mobile resources through
MEC, the energy consumption and latency in communication networks in the
Metaverse can be significantly saved.

###  II-E Blockchain and Cryptocurrency

Blockchain, which is a distributed ledger, first appeared in 1991. Blockchain
became widely known when Bitcoin emerged in 2009. It stores digital data in
blocks, and the blocks are strung together via cryptography. The data is time-
irreversible by leveraging cryptographic hash functions. With the merits of
transparency and security, blockchain has numerous prospective applications in
finance, government, commerce, etc. In the Metaverse, blockchain technology
can be utilized to build a decentralized virtual world, as shown by Kang et
al. [7]. To protect user privacy, cryptocurrencies are also essential in the
Metaverse. Cryptocurrencies (e.g., Bitcoin, Litecoin, Ethereum) are powered by
blockchain, and Bitcoin is the most well-known cryptocurrency. Additionally,
cryptocurrencies are not physical, they exist only in the decentralized
network, and their creation is determined by an algorithm (or protocol). The
benefits of using cryptocurrencies in the Metaverse include:

  * •

Transactions are recorded permanently.

  * •

Privacy is protected. There is no third-party bank verifying transactions, so
users do not provide sensitive information.

  * •

Fairness and transparency can be achieved since transactions can be
scrutinized.

Blockchain also enables play-to-earn games. Players can earn cryptocurrencies
or digital assets through these games. Financial incentives can stimulate more
users to join the game. The more users participate in the game, the more
valuable the assets accumulated by senior players will be. Hence, incentive
mechanisms can be incorporated into play-to-earn games to attract more users.
This breathes new life into the Metaverse world as well since some people
could make a living on the revenue of these games.

##  III Challenges for FL-MAR in the Metaverse

This section discusses the potential challenges of FL-MAR systems in the
Metaverse.

###  III-A Limited communication resources

The demands of bandwidth in the Metaverse are much more significant than
current ordinary games and social entertainment. This is because the Metaverse
virtual world has to render massive surroundings (e.g., trees, flowers),
buildings, avatars (or people), etc., simultaneously.

Besides, to achieve the immersive experience in the Metaverse, haptic
technology is indispensable to create the experience of touch and receive the
reactions of the virtual world. Haptics technology allows only 1 ms of
latency. Yet, current LTE networks only sustain around 25 ms of latency [8].
Thus, limited communication resources are one of the barriers to the
widespread deployment of the Metaverse. To address the limitation,
technologies mentioned in Section II could be utilized, such as semantic
communications and MEC. Semantic communication can lower the data traffic, and
thus help to build an efficient communication network. MEC, located at the
edge of mobile networks, can reduce the communication latency and assist
mobile devices in handling complex tasks that exceed their capabilities.

###  III-B Limited computation resources

The Metaverse will generate a vast volume of data, and thus it is in dire need
of powerful computing resources [9]. The resource-constrained devices worn by
users not only have to train their own FL model but also are responsible for
processing newly generated data and converting the raw data into the 3D
virtual world. Apparently, today’s mobile devices do not have the computing
capability to finish those complex tasks efficiently.

This existing obstacle might be addressed by better mobile edge computing
mechanisms. Mobile edge computing assists applications that have requirements
of low latency and high bandwidth close to the data source. For example,
devices can offload complex tasks to edge servers in proximity to save energy
consumption and computation resources. Since edge servers are much closer to
users than cloud servers, they could provide services with low latency, which
is suitable for the Metaverse to provide a real-time and stable immersive
VR/AR experience. However, if the computing tasks are pretty complex and
energy-consuming, they can be uploaded to cloud servers. Hence, the
hierarchical cloud-edge-device computing paradigm can be utilized. Cloud
computing provides robust computing and storage resources. The appropriate
combination of edge- and cloud-based applications is essential to maximize the
system performance.

###  III-C Security and Privacy

Our physical world is being transformed into a digital one as time passes. In
the Metaverse, people’s lives are changing in the areas of shopping,
education, tourism, medical care, etc. There will be new forms of security
risks, including the threats of scams, identity leakage, and data protection.
Although FL can protect user privacy and data security to a certain extent,
users in the Metaverse still expose their sensitive information to the virtual
world. Besides, in FL, the model parameter transmission has the potential to
leak user privacy. In the following, some security techniques are discussed.

  * •

Differential privacy (DP). It can be seen as a way of the mathematical
definition of privacy. If an algorithm is differentially private and it
publishes aggregation information about a database, others cannot predict from
the output whether any data record of a specific individual was recorded in
the original dataset or not. DP ensures the individual information in the
database will not be compromised. Additionally, DP has been applied to the
industry. For instance, Apple adopts local DP to behavior analytics of iPhone
users [10].

  * •

Secure multi-party computation (SMC). It is a cryptographic protocol in that
several parties jointly compute an agreed function without exposing the data
of each party. In SMC, data can be shared distributedly with other parties
without the need for a third-party organization and is still under protected.
Hence, SMC can be deployed in distributed systems in the Metaverse.

  * •

Homomorphic encryption (HE). HE is an encryption technique that enables users
to perform calculations without having to decrypt the data. When the resulting
computations are decrypted, they produce the same output as the result
calculated by the unencrypted data. Therefore, if outsourcing data to a third
party for analytics, HE can be utilized to ensure the data will not be
analyzed in its original form.

  * •

Consensus mechanism. It could be any mechanism which is fault-tolerant and
usually used in blockchain systems to reach a consensus on a data value or a
network state among distributed systems (e.g., cryptocurrencies). Hence, a
consensus mechanism is frequently used to attain trust and security among
distributed parties.

##  IV Applications of FL-MAR in the Metaverse

This section lists some scenarios in which MAR and FL are applied to the
Metaverse.

###  IV-A Autonomous Driving

According to the National Motor Vehicle Crash Causation Survey (NMVCCS)
conducted by the National Highway Traffic Safety Administration of the United
States, 94% of NMVCCS crashes were caused by drivers [11]. Hence, autonomous
vehicles are becoming a feasible solution for transportation in the future.
Recently, deep learning has been a popular approach to the application of
autonomous driving in terms of object detection, obstacle avoidance and so
forth. Considering the fact that the capabilities of hardware storage and
computation are improving, training models locally is not only beneficial for
data security and user privacy but also reduces network energy consumption and
latency.

Fig. 3 depicts that several autonomous cars are driving on the road while
training their models locally in the Metaverse. Since different cars
experience various environments, such as weather and lighting conditions,
incorporating FL into this scenario will help each vehicle build a more
accurate model.

Figure 3: The architecture of autonomous driving with FL in the Metaverse.

Additionally, since Metaverse has immediate physical-virtual world interaction
characteristics, it can simulate various kinds of driving situations,
including some rare cases. Therefore, it helps test whether self-driving cars
are safe and reliable in various extreme conditions. Besides, it is no longer
an unrealistic fantasy. Oxbotica, which is an autonomous vehicle software
company, announced its AI-powered software MetaDriver in June 2022. MetaDriver
collects plentiful scenarios to test and improve autonomous vehicle behaviours
in the Metaverse without physically driving them [12].

###  IV-B Shopping

Online shopping has become a part of people’s lives. As the number of MAR
applications grows, online shopping also enjoys this convenience. For example,
IKEA, the company that sells ready-to-assemble furniture, appliances and home
services, equips its app with AR capabilities. The AR function allows users to
place 3D models of equal scale with the real size virtually in their homes,
which facilitates online shopping and saves users’ time. Adidas also launched
the AR footwear try-on function in its iOS app. Fig. 4 illustrates these two
example applications.

Figure 4: Two examples of MAR applications.

However, it is evident that the MAR applications still need improvement. Each
person has different looks and various living places. Therefore, by
incorporating FL, the mobile device can learn its own model to fit the
specific person. Additionally, since the Metaverse can mix the virtuality with
the real world, people will reveal similarities to their real lives in the
virtual worlds due to more time spent virtually. Hence, more new shopping
models will appear in the future. Products such as digital clothing,
furniture, cosmetics and so forth may have a similar status to purchases in
the real world.

###  IV-C Education

The Metaverse will transform the educational environment in the future [13].
Different from traditional in-person learning and online learning, Metaverse-
based learning will be an environment which is a mixture of the virtual and
real world. It allows students to interact with each other in a virtual and
decentralized setting and join various complex learning activities. In light
of the profusion of online learning experiences during the Covid-19 period,
Metaverse-based learning is much needed now. For example, in geography
classes, students can immerse themselves through VR headsets in geography and
experience the differences between different climates in different regions.

##  V Case Studies of FL-MAR in the Metaverse

In this section, we present two case studies of FL-MAR in the Metaverse. One
FL-MAR system’s channel access scheme is FDMA, and the other one uses NOMA. We
study the system from the perspective of how to optimize the resource
allocation in the system to save energy and time consumption.

###  V-A FDMA-enabled FL-MAR in the Metaverse

First, we investigate a basic FL-MAR system via FDMA. In [14], we formulate a
weighted sum of total energy, time consumption and accuracy by using three
weight parameters. We optimize the allocation of the bandwidth, transmission
power, CPU frequency setting and MAR video frame resolution for each
participating mobile device in the Metaverse. By setting different weight
parameters, our resource allocation algorithm can adapt to different
requirements of the FL-MAR system, either time-sensitive or energy-hungry.

We assume there are $40$ users in the system. Fig. 5 contains two subfigures.
One shows the total energy consumption, and the other is the total time
consumption. We choose three pairs of weight parameters to compare our
resource allocation algorithm with a random allocation strategy under
different maximum transmit power limits. Note that $w_{1}$ is the weight
parameter of energy consumption, and $w_{2}$ is the weight parameter of time
consumption. The weight parameter of the model accuracy is fixed because we
focus on the energy and time consumption here. If $w_{1}$ (resp., $w_{2}$)
becomes larger, our resource allocation algorithm will emphasize minimizing
the energy cost (resp., time consumption). Hence, it can be seen obviously
from each bar that as $w_{1}$ (resp. $w_{2}$) increases, the energy (resp.
time) consumption will decrease. In addition, the random strategy adjusts all
video frame resolutions as the minimum, and thus it saves much computation
energy consumption while sacrificing the model accuracy. Considering this
condition, our algorithm still achieves better total energy consumption than
the random strategy, even at $w_{1}$ = 0.1 (i.e., the algorithm mainly
stresses the minimization of the total time consumption). In short, the
results clearly illustrate the superiority of our proposed joint optimization
algorithm.

Figure 5: FDMA-enabled FL-MAR system: simulation results under different
transmit power limits.

###  V-B NOMA-enabled FL-MAR in the Metaverse

Here, we study the NOMA-enabled FL-MAR system in the Metaverse. We also devise
a resource allocation algorithm and show the validity of our algorithm, which
jointly optimizes the weighted sum of energy and time consumption. Assume
there are $40$ users and $20$ channels. There are $2$ users multiplexed on one
channel.

Figure 6: NOMA-enabled FL-MAR system: simulation results under different
transmit power limits.

In Fig. 6, we compare three pairs of weight parameters
$(w_{1},w_{2})=(0.9,0.1),(0.5,0.5)$ and $(0.1,0.9)$ with a random allocation
strategy. $(w_{1},w_{2})=(0.9,0.1)$ refers to situations when devices are low-
battery. $(w_{1},w_{2})=(0.5,0.5)$ stands for the case of equal consideration
of optimization energy and time. Besides, $(w_{1},w_{2})=(0.1,0.9)$ stresses
the minimization of total time consumption.

Fig. 6 contains comparisons under different maximum transmission power limits
of the total energy consumption and time consumption. It can be concluded that
when the maximum transmission power increases, total time consumption slightly
decreases. Due to the expansion of the range of the maximum transmission
power, there will be a more optimal solution to decrease the time consumption.
Our resource allocation algorithm performs better than the random allocation
strategy in the aspect of energy optimization. In terms of total time
consumption, the proposed algorithm performs worse than the random allocation
when $(w_{1}=0.9,w_{1}=0.1)$. This is because the case of $w_{1}=0.9$
emphasises more about the energy optimization and less about the time
minimization. The simulations show the effectiveness of our approach for
different weight parameters.

###  V-C Analysis of the difference between FDMA-enabled and NOMA-enabled FL-
MAR system

It could be concluded from Fig. 5 and Fig. 6 that, in terms of total energy
consumption, there is little difference between the performance of FDMA-
enabled and NOMA-enabled FL-MAR system. Regarding total time consumption, the
FDMA-enabled system performs slightly better when $w_{1}=0.9$ and $w_{2}=0.1$.
When $(w_{1}=0.5,w_{2}=0.5)$ and $(w_{1}=0.1,w_{2}=0.9)$, their performance is
similar. In addition, the random scheme under NOMA outperforms the random
scheme under FDMA. From the simulation results and our theoretical analyses,
when the system has ample channel resources, careful optimization of FDMA has
comparable performance with NOMA, so there is no need to implement the more
complex NOMA for resource-rich scenarios. Yet, when the system has limited
channel resources, NOMA can improve the system performance by leveraging
power-domain orthogonality. Our findings are also consistent with recent
results in the literature [15]. In Metaverse applications where devices demand
considerable communication resources, designing NOMA to improve system
performance requires not only simulation but also real-world experiments. We
hope our preliminary simulation can motivate more real-world NOMA experiments
for the Metaverse in the research community.

##  VI Conclusion

In conclusion, this article gives insights into the necessity and rationality
of a federated learning enabled mobile augmented reality system (FL-MAR) in
the Metaverse. With the development of mobile devices, they can support more
and more complex tasks and operations. The combination of FL and MAR in the
Metaverse not only helps protect user privacy to a certain extent but also
utilizes the available computing resources on mobile devices. Besides, this
article lists and explains the promising technologies that enable FL-MAR
systems in the Metaverse. For example, MEC can be integrated into FL-MAR
systems to perform heavy tasks for users and reduce network latency.
Blockchain and cryptocurrencies facilitate the functioning of the Metaverse
world in terms of commerce, entertainment, etc. Some application scenarios are
also given in this article, including autonomous driving, shopping, education
and so forth. Finally, two case studies are evaluated. One is FDMA-enabled,
and the other uses NOMA. We envision our paper to motivate more research on
leveraging FL for the Metaverse, and designing more efficient channel access
mechanisms to enable the Metaverse for mobile user equipments.

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## Biographies

Xinyu Zhou is currently pursuing a Ph.D. degree at Nanyang Technological
University (NTU) in Singapore. Her research interests include federated
learning and Metaverse.

Jun Zhao is currently an Assistant Professor in the School of Computer Science
and Engineering at Nanyang Technological University (NTU) in Singapore. He
received a PhD degree in May 2015 in Electrical and Computer Engineering from
Carnegie Mellon University (CMU) in the USA (advisors: Virgil Gligor, Osman
Yagan; collaborator: Adrian Perrig), affiliating with CMU’s CyLab Security &
Privacy Institute, and a bachelor’s degree in July 2010 from Shanghai Jiao
Tong University in China. Before joining NTU first as a postdoc with Xiaokui
Xiao and then as a faculty member, he was a postdoc at Arizona State
University as an Arizona Computing PostDoc Best Practices Fellow (advisors:
Junshan Zhang, Vincent Poor). His research interests include federated
learning, edge/fog computing, and Metaverse.