FedTour: Participatory Federated Learning ofTourism Object Recognition Models with Minimal
Parameter Exchanges between User Devices
Shusaku Tomita1,3 , Jose Paolo Talusan1 , Yugo Nakamura2 , Hirohiko Suwa1,3 , 1 , Keiichi Yasumoto1,3
1

Nara Institute of Science and Technology, Nara 630-0192, Japan
2
Kyushu University, Fukuoka 819-0395, Japan
3
RIKEN Center for Advanced Intelligence Project AIP, Tokyo 103-0027, Japan
Email: http://ubi-lab.naist.jp

Abstract—In this paper, we propose FedTour, a federated
learning-based method for training tourism object recognition
models, which utilizes short-distance direct communication between user devices and maximizes the model performance within
a limited number of updates. In FedTour, whenever two user
devices are within range, they first exchange metadata including
the learning degree (e.g., recognition accuracy) of their models,and determine whether it is effective to integrate the peer modelby using a regressor trained with various pairs of models withdifferent accuracy to predict the accuracy of the merged model.
Once it is deemed effective, the model parameters are exchanged
and the model is updated using FedAvg (averaging weights of
two models of user devices). By carefully setting the thresholdof whether FedAvg is applied or not, model performance isimproved within a limited number of model parameter exchanges
resulting in lower power consumption of user devices. We
conducted a simulation using mobile phone trace data of actual
users in a real sightseeing area and evaluated the improvementin accuracy of a CNN model that recognizes 10 objects while
limiting the number of model parameter exchanges to only 40.
Results show FedTour increased the initial model accuracy by
112%, while the baseline gossip-based method achieved 69%.
Index Terms—Federated learning, Tourism object detection,
Participatory learning, Direct communication

I. I NTRODUCTION
In recent years, AI technologies have been utilized for
tourism services. There have been many AI-based systems[1], [2] that recommend sightseeing spots from photos uploaded to SNS. “Deaps,” a smartphone application that recommends tourist spots and provides tourist information basedon information posted by users on SNS and their behavioral
history, has been developed. Smart tourism relies on adopting
emerging technologies such as social media and AI to create
new value propositions [3]. The White Paper on Tourism
in Japan, 2021* by the Japan Tourism Agency, Ministry ofLand, Infrastructure, Transport, and Tourism, reports that new
and diverse travel styles such as staycation, decentralized
travel, nearby travel, and online tours have been gaining
popularity. Thus, the development and utilization of tourism
AI will accelerate in the future to adapt to the diversity of
tourists/tourism styles.
* https://www.mlit.go.jp/kankocho/news02 000447.html

One of the main functions of tourism AI is context recognition of tourist attractions (e.g., congestion level, availabilityof events, weather, scenery, etc.) in real-time. A promising
method is the use of object recognition models to comprehensively estimate the context from a set of objects in photos
and videos. A tourist spot contains unique attractions ranging
from wild animals such as deer, unique architecture such as
temples and shrines, and scenery such as cherry blossoms orautumn leaves. Building a recognition model for a wide varietyof objects is non-trivial. A model has to learn vast amountsof data which is often held by large companies or stored onusers’ devices. Privacy and security concerns make collecting
data difficult.
Federated Learning [4] has attracted a great deal of attention in recent years because it has the potential to trainmodels while addressing privacy and security concerns. In thismethod, local models are trained on local datasets, and only
parameters (e.g., model weights or gradients) are exchanged
between clients to achieve a global model. Aggregation servers
orchestrate the entire training process and update the global
model without the need to access local client data. The process
makes it hard to leak private information. When Federated
Learning is applied to tourism object recognition models, photographs taken by tourists are used as training data. The moretourists participate, the more diverse the tourist object recognition models that can be obtained. However, in addition to
the maintenance and operation cost of the aggregation server,
there are also additional communication costs (communication
fee, power consumption, etc.) because of the frequent largecapacity wide-area wireless communication with the server.
Therefore, to create a tourism object recognition model basedon federated learning, it is necessary to solve the following
challenges: (1) avoid using wide-area wireless communication
and aggregation servers, and (2) select a communication target
with a model that will effectively maximize the accuracy ofthe merged model within a limited number of model updates(limiting the communication cost).
Several methods have been proposed to solve these challenges. Lee et al. [5] trained models by directly communicatingwith nearby devices instead of relying on servers. Chen etal. [6] reduced federated learning’s dependency on aggregation
servers by having each device execute a process similar tothat of the aggregation server. However, these are insufficient
to solve the above problems because they either require more
frequent communication to achieve higher accuracy or stillrequire the existence of an aggregation server.
In this paper, we propose FedTour, a participatory federated
learning scheme for tourism object recognition model, thatsolves the challenges (1) and (2). For challenge (1), we employ
short-distance direct communication between user devices as[5]. For challenge (2), we develop a novel method allowing
each user device to know how effective it is to integrate withthe peer model while only exchanging a small amount of
metadata with the encountered user device.
To develop the method, we collected accuracy data of object
recognition models created by applying FedAvg [4] to combinations of two models with different accuracies and trained amodel to predict the accuracy of the merged model from only
the accuracy information of the two original models. We use
this predicted accuracy to determine if the local model would
be improved in the resulting update. The model parameters
would be exchanged only if the predicted accuracy is greaterthan the local model by a predetermined threshold. Thus, it
is expected to reduce communication costs and increase thepossibility of improving the accuracy within a limited numberof parameter exchanges between user devices.
Contributions: This paper presents, FedTour, a participatory federated learning algorithm for training models withminimal parameter exchanges between devices. This paper’s
main contributions are as follows:
Train models without the need for aggregation servers,
using instead short-range direct communication (WiFi
Direct, BLE, etc.) between tourist devices.
• A model update algorithm that takes into account both the
movement of tourists and the predicted accuracy of theresulting model before any parameter exchange occurs.
This improves local model accuracy while limiting thenumber of model exchanges required for training.
• A simulation that shows how the proposed algorithm performs against a gossip protocol-based method in whichmodels are exchanged randomly, without regard for the
resulting model accuracy. It was found that the proposed
method improves upon the initial recognition model by
112% compared to the gossip-based method which only
showed a 69% improvement.

•

II. R ELATED W ORK
Federated Learning is one of the machine learning methods
proposed by Google. It is capable of learning on private datasuch as personal photos and search histories, which are difficult to train a global model on. The first Federated Learning
method proposed was Centralized Federated Learning, which
consists of an aggregation server and edge devices. Later,
Decentralized and Distributed Federated Learning methods
were proposed.

A. Decentralized Federated Learning
Decentralized Federated Learning places an intermediate
server or device as a relay point between the central server
and the edge devices, thus reducing the direct load on theaggregation server. In the methods [6]–[9] using Decentralized Federated Learning, the number of accessible devices isincreased by setting up a relay point, so that a wider rangeof edge devices can participate in federated learning. There isanother approach [10] that uses multiple central servers andupdates their global model obtained from each local network
while communicating between servers.
B. Distributed Federated Learning
Distributed Federated Learning mainly utilizes connections
between edge devices. This learning method is categorized
into two types: one targets the fixed network topology (e.g.,links between devices always exist like Peer-to-peer network)
between devices [11]–[14], and the other does the topology
which dynamically changes (e.g., existence of links between
devices is opportunistically changed) [5], [15], [16]. The distributed federated learning eliminates the need to install servers
and allows an unspecified number of devices to participate in
the training. However, due to the push-only nature of these
protocols, a device may end up merging its fresher modelwith an outdated one. In this study, we consider the devices
possessed by tourists’ (e.g., smartphones) as edge devices, andsince we assume that many tourists come and go in the tourist
spots, we assume the latter network.
Among the existing studies in this category, Opportunistic Federated Learning [5] is the only other approach that
implements encounter-based pairwise collaborative learning.
They also use short-distance direct communication between
devices, making learning possible without using wide-areacommunication. Model integration only occurs when it isbeneficial and feasible, increasing training efficiency. However,while their approach modifies the number of training rounds
per encounter based on the duration of the encounter, it does
not put a ceiling on the total number of parameter exchanges
and model integration. This may cause the user device toconsume more power due to continuous integration as theuser encounters other devices. Our approach can limit the
number of exchanges a device will make, lowering the power
consumption on the user device, while maintaining high model
performance.
III. P ROBLEM S TATEMENT
Our primary goal is to implement a tourism object recognition model based on Federated Learning with user participation. In this section, first, we show a use case scenario andidentify challenges to solve. Then, we formulate the problem.
A. Use Case Scenario
Suppose tourists will be visiting, Nara Park, a well-known
tourist area in Japan. There are three famous sightseeing spots,
Nara Park, Todaiji Temple, and Kasuga Taisha Shrine. Eachof these tourist spots features a set of objects that uniquelycharacterize the area. These objects are denoted as ON P , OT T ,and OKT , respectively.
ON P = {male deer, female deer, fawn,...}OT T = {statue of Buddha, pond, carp, fawn,...}
OKT = {wisteria, torii, pond, carp, shrine,...}
We assume that each of these spots also has a signage
device installed, cN P , cT T , and cKT . We assume that thesehave no training data, but have the baseline models (trained bypublic data or given from tourist devices) that can recognize
the objects within their areas.
Let there be nine tourists who will travel aroundthese sightseeing spots. These tourists own mobile devices,c1 , c2 , c3 , c4 , c5 , c6 , c7 , c8 , and c9 , respectively. The tourists
download a base model trained on a limited number ofsightseeing objects beforehand (from signage or the Internet).
They train this base model on any of their annotated data, if
available. Fig. 1 shows the path of the tourist c1 through thedifferent sightseeing spots. As the tourist c1 travels between
locations (Nara Park to Todaiji Temple and finally to Kasuga
Taisha Shrine), s/he comes into contact with other tourists
and signage. At each of these events, both the tourist andthe other tourist/signage iteratively update their own models
through parameter exchanges.

Fig. 1: Contact between c1 and each device
B. Challenges
In the prior scenario, if conventional Federated Learning [4],[17] is used for model updates, an aggregation server that canbe accessed by all devices is required. However, as the numberof tourists increases, the load on the server and the network
bandwidth required to access it becomes saturated. When thisoccurs, the aggregation server limits the number of models
that can be trained and reduces the update frequency.
A CNN model for image classification is used as the tourism
object recognition model. Updating models such as these
consume a large amount of bandwidth. Lee et al. [5] foundthat the communication process consumes a lot of power
because direct communication is performed multiple times
during training between devices. Since tourists do not always
have access to methods to charge their devices, it is necessary
to reduce the power consumed by these model updates.
For these requirements to be met, the following challenges
must be solved:1) Update models without the use of wide-area wirelesscommunication and aggregations servers.

2) Maximize the model accuracy while minimizing thenumber of parameter exchanges.
C. Problem Formulation
We define the assumed environment and summarize thenotations used in Tab. I.
TABLE I: Elements in the Assumed Environment
ElementAAc
Oa
Oc
Cstationary
Cmobile
C
R
Dc
D
Mc
Wc
T
pos(c, t)cn(c, c′ , t)CN

Description
Set of tourist area
Target area for enhanced cognitive ability of c
Set of recognized objects in a ∈ A
Set of recognition enhancement objects in c
Set of fixed devices
Set of mobile devices
Set of all devices
Communication range
Set of data in c
All data
Model in c.
Model parameter of Mc
Set of time t in the assumed environment
Position of c at time c.
Contact of c, c′ at time t.
Contact of all devices

We consider A to be a set of sightseeing areas. In eachtourist area a ∈ A, there is a set Oa of tourist objects tobe recognized. Oa can be dynamic objects (animals, crowds,
stalls, etc.) or fixed objects (buildings, gates, trees, etc.). There
are multiple tourists moving within and travelling between
different tourist areas in A. Each tourist is assumed to havea single mobile device such as a smartphone, c ∈ Cmobile ,where Cmobile is the set of mobile devices. In addition, fixed
devices such as signage are placed in the sightseeing area,
and the set of these devices is denoted by Cstationary . The
set of devices C in the assumed environment A is denoted byequation (1).
C = Cstationary ∪ Cmobile(1)

The device c ∈ Cmobile changes its position in the environment at every timeslot t ∈ T . We denote the position of adevice c at t as pos(c, t). If pos(c, t) and pos(c′ , t) are withincommunication range R, they are considered to be in contact.
Contact between c and c′ is denoted by cn(c, c′ , t). The set of
all contacts CN then is denoted by equation (2).
[CN =cn(c, c′ , t)(2)c,c′ ∈C,t∈T

Each mobile device c ∈ Cmobile has training data, while
fixed devices c ∈ Cstationary do not. We denote this local
data in each c ∈ C as Dc , where Dc ̸= ∅ for c ∈ Cmobile and
Dc = ∅ for c ∈ Cstationary . All the data in this environment
are defined as D by equation (3).
[
D=
Dc
(3)
c∈Cmobile

Each c ∈ Cmobile has a tourist object recognition model Mc
trained on its own data Dc . These weights Wc of Mc will beaveraged with Wc′ of other tourists. The model Mc in eachc has a tourist area Ac ⊆ A to be recognized, and the set of
tourist objects Oc is defined as the expression (4).
[
Oc =
Oa
(4)
a∈Ac

When two tourists come into contact at time t a parameterexchange may occur. When a tourist is in range of two or moreother tourists, they choose at most one of them to communicate
and exchange parameters with. We assume that parameterexchange of the models between devices are completed withintimeslot t (we assume enough time width such as 30 seconds
for each time slot t). This is defined by Equation (5) using
the binary variable xcn(c,c′ ,t) . xcn(c,c′ ,t) is a variable thatindicates the presence or absence of communication between
devices, xcn(c,c′ ,t) = 1 when communication (model parameter transmission) occurs from device c to another device c′ ,and xcn(c,c′ ,t) = 0 otherwise.
We assume that the model parameter transmission andreception by each device can occur at most once per timeslot as follows.
X

xcn(c,c′ ,t) ≤ 1, ∀c ∈ C, ∀t ∈ T(5)cn(c,c′ ,t)∈CN,c̸=c′

We also limit the number of times the model parameter of
each device can be exchanged, defined as L. This is done inconsideration of power consumption suppression. Equation (6)expresses this constraint.
X∀c ∈ C,xcn(c,c′ ,t) + xcn(c′ ,c,t) ≤ L(6)cn(c,c′ ,t)∈T

The goal of the proposed approach is to maximize themodel improvement with a minimal number of parameterexchanges. This is done by selecting only contacts which
will effectively improve the accuracy of the local model. Thisdegree of improvement of the model accuracy after integrating
the parameter Mc′ into Mc is given by Improve(Mc , Mc′ ).
This objective function is expressed in Equation (7).
M aximize

X X X

X

c∈C c′ ∈C{c} t∈T cn(c,c′ ,t)∈CN

xcn(c,c′ ,t) · Improve(Mc , Mc′ )(7)subject to (5) − (6)The problem defined above is NP-hard or in more difficult
problem classes since it implies the Knapsack problem as a
special case even when the trajectory of each mobile device
is known in advance.
IV. F ED T OUR
To solve the problem defined in the previous section, we
propose FedTour, a novel and efficient model update method
based on distributed federated learning. FedTour reduces
unnecessary parameter exchanges by predicting the object

recognition accuracy of the merged model even before thenode and the peer node exchange parameters.
A. Investigation of Impact of Model Integration
The purpose is to predict the accuracy of the merged
model with as small amount of information as possible before
merging two models because the parameter size of CNN
models is huge (e.g., 60 MB) in general.
To investigate the impact of FedAvg-based model integration, we checked the effect of the integration on accuracy
improvement. We created a large number of CNN models
with various recognition accuracy (trained with different datain the same dataset), integrated any pair of models, andchecked the number of pairs whose accuracy was improved.
We employ a simple integration method where all model
parameters are simply averaged between two models. The
model to be integrated uses VGG16, and fine tuning of the
convolutional layer is performed. For fine tuning, we usedparameters already learned in ImageNet.
In this study, we focused on the 10-class classification
problem as a feasibility study to validate the effectiveness of
our method. Since there is a limit to the number of niche
objects available at each tourist attraction, we believe that a
10-class classification problem is a reasonable first step. Thus,
we used CIFAR-10 dataset† , which includes 60,000 images
consisting of 10 types (classes) of objects. We split the data
into 50,000 and 10,000 images for training and evaluating
the integrated model respectively. To maintain the diversity inaccuracy of detecting each class, we created multiple datasetswith a different number of images per class. To keep thediversity in accuracy of detecting each class, we createdmultiple datasets with a random number of images per class.
The number varied from small (0-1000), medium (1001-3000)
and large (3001-5000).
Among 310 combinations (three categories for each of
10 classes), 66 diverse combinations were selected, and foreach combination, we created three datasets, resulting in 198
datasets. Also, for models with significantly lower accuracy,
we added 33 more datasets with (0-10) or (11-100) additional
data per class. In total, 231 models were created and theaccuracy of the integrated model was recorded.
Out of 53,361 (231×231) pairs of models, the integration
improved the accuracy of 37,315 pairs. The integration offersa chance of accuracy improvement for the integrated model.
B. Accuracy Prediction
Based on the integration results in the previous section, weconstructed a support vector regressor that predicts the change
in the accuracy of the user’s model after integration based on
the accuracies of two initial models. To train the regressor,
we used the difference in accuracy between before and after
integration of two models as prediction value, and accuraciesof the two models as the input value. The pair of prediction
and input values can be obtained from the integration results.
† https://www.cs.toronto.edu/ kriz/cifar.html

Algorithm 1 FedTour Algorithm{Wct , Qtc , t}

Input:
1: threshold ← getT hreshold();
2: txn ← numT ransmission(Qtc , L);
3: rxn ← L − txn;
4: continueT our ← true
5: while continueT our do
6:
if cn(c, c′ , t) then7:
if txn > 0 then8:
sendM etadata(c′ , Qtc );9:
if T xRequested(c′ ) then10:
sendW eights(c′ , Qtc );11:
txn ← txn − 1;12:
end if13:
end if
14:
Qtc′ ← recvM etadata(c′ );15:
Q′ ← Qtc + accuracyRegressor(Qtc , Qtc′ )16:
if Q′ > threshold then17:
if rxn > 0 then18:
Wct′ ← RecvW eights(c′ )19:
Wct+1 ← f edAvg(Wct , Wct′ )20:
rxn ← rxn − 1
21:
end if22:
Qt+1← evaluate(Wct+1 )
c
t+1
23:
if (Q
> threshold) ∨ (Qt+1> Qtc ) thenc24:threshold ← increaseT hreshold(Qt+1
)
c
25:
else
26:
threshold ← decreaseT hreshold(Qt+1)
c27:
end if28:
end if29:
end if30:t ← t + 1;
31:
continueT our ← decideContinueT our(c, t)
32: end while

Thus, the regressor is trained with 53,361 integration patterns.
The trained regressor achieved mean absolute percentage error(MAPE) under 6%.

D. FedTour Algorithm
Algorithm 1 shows how FedTour is run on each devicec. It takes model parameters Wct and accuracy Qtc of c’s
model at the present time t as inputs. At line 1, it firstsets the appropriate threshold. It will update the model withthe peer model parameters only when the merged model’s
accuracy exceeds this threshold. The threshold is determinedby the function getT hreshold()‡ as a median value of the
distribution of model accuracy of all devices C. If Qtc isgreater than the median value, the threshold is set to Qtc (sothat the model accuracy is improved by update). At lines 23, the limit of parameter exchange times L is distributed to
the limit of transmissions txn and the limit of receptions
rxn. We use the function numT ransmissions() to determine the value for txn. We empirically employ the formulatxn = L × (Qtc − Qmin )/(Qmax − Qmin ) where Qmax and
Qmin are maximum and minimum accuracy of the models inall devices C. The main loop of the algorithm is between lines
5–31. Whenever a contact cn(c, c′ , t) happens, c sends themetadata (i.e., Qtc ) and the model weights (Qtc ) if requested,
to the peer device c′ at lines 7–13. At lines 14–28, it tries to
update the model. At line 14, the metadata (model accuracy)
Qtc′ of the peer device is received. This is used as input inaccuracyRegressor(Qtc , Qtc′ ) which predicts the accuracy of
the updated model Q′ . If the accuracy of Q′ is higher thanthreshold, then the peer model’s weights Wct′ are received
and the updated weights Wct+1 are obtained by averaging Wct
and Wct′ . At lines 22–27, threshold is increased or decreased
depending on the result of update. When the updated model’s
accuracy is higher than the original model, then thresholdis increased to continue improving the accuracy of the modelin the future. Otherwise, threshold is decreased so that the
update probability will be higher in future encounters.
V. S IMULATION EXPERIMENT

C. Peer Model Selection
As tourists move through the area, their models will be
constantly integrating other users’ models. This method ofmodel integration allows models to iteratively update andtrain with the goal of improving the accuracy. However, thiscan be ineffective, requiring more integration to reach higher
accuracy.
In the proposed method, we set a threshold for the target
accuracy of the integrated model. Each user device performs
model integration only if the predicted accuracy of the integrated model exceeds this threshold.
The regressor in IV-B is used for this purpose. First,
when a user device encounters the other user’s device, theyexchange only the accuracy information of their models andpredict the accuracy improvement that would be gained byintegrating each other’s model. Then, either (both) of thedevices requests the peer device’s model parameters only if
the threshold constraint is satisfied. Finally the device receives
the parameters and integrates them by the integration in IV-A.

To evaluate the proposed method, we conducted a model
update simulation based on mobile phone trace data.
A. Overview of Simulation and Evaluation
In the simulation, we evaluate the change in the average
accuracy of all models when the number of model parameter exchanges is limited. Fig. 2 shows the overview of thesimulation. Each user is provided a CNN model for image
classification and model updates are done via simple averaging
as described in Sect. IV-A. threshold is initially set to themedian of the overall model accuracy for all users or to theaccuracy of the local model if it is higher than the median.
The decreaseT hreshold and increaseT hreshold functions
modify the threshold to 0.9 and 1.1 times the current value
respectively. txn is set to vary in proportion to the accuracyof the model.
‡ We assume that each device uploads its current model accuracy to thecloud server when the application is activated and the sever has the distributionof model accuracy of all devices in the target area A.

Algorithm 1 is then run on all user devices. We compared
the proposed method to a gossip method that randomly performs the model parameter exchange with the encountereduser with a probability of 10%. We conducted the simulation
with these two methods for each contact in chronologicalorder. Since the model is about 58.9MB of data and 18
transmissions or receptions are about 1GB, we set the limit
of model parameter exchange times L to 40 to keep the communication volume within 2.5GB. The proposed method andthe gossip method each have different methods for setting thenumber of reception and transmission limits. In the proposed
method, reception limit rxn and transmission limit txn aredetermined by dividing L = 40 based on the formula describedin Sect. IV-C. In the gossip method, rxn and txn are set tohalf of the limit L = 40. After the simulations, we calculated
the average accuracy of all models and evaluated the changes.
We also evaluated the average accuracy when L was set to 20,
30, 50, 70, and 100 times.
Tourist position at
12:00:00

Tourist position at
12:00:10𝑐$Tourist position at
12:00:20

𝑐$

𝑐$
𝑐#

𝑐"

Communication
range (50m)

Fig. 4: Initial accuracy distribution of the models

𝑐"

𝑐"

𝑐#

𝑐#
𝑐!

Fig. 3: Simulation area𝑐!

𝑐!

Fig. 2: Overview of simulation experiment. For each time slot,
we use the coordinates of each user to find which users arepass by each other. Each user is assumed to only be able tocommunicate with devices within a radius of 50 meters. If
there are users that pass each other in a time slot, they will
execute the proposed algorithm.
B. Mobile Phone Trace Data
In the simulation experiment, we used the “point-type flow
population data” [18] provided by Agoop Inc. We extracted
users, who were considered to be tourists, from the trace datain the area of Fig. 3 during the time period of 6:00 to 18:00
from October 31, 2020 to November 30, 2020, and obtained
data for 1,900 users. Since the number of users per day issmall and it is not possible to trace users for more than twodays, we treated the data for one month as the data for one
day (assuming that Agoop data covers about a few percentageof the actual population). In addition, if a user was within a
50-meter radius other users, we considered them as having
made contact.
C. Model Assigned to Each User
We built a model for 1,900 people for the simulation. The
model used was VGG16. For the dataset we used CIFAR-10
instead of the tourist photos. 50,000 out of the 60,000 images
in CIFAR-10 were used for training, and the training data
were distributed to each model while avoiding duplication.
When these data were assigned to each user, the accuracyof the entire model became low, so we expanded the data to
500,000 images (by applying rotation, magnification, etc). The
remaining 10,000 images were used to evaluate the accuracyof each model in simulation. It is expected that many users
will have only a few images in the real environment. The
distribution of data to each model was made so that the numberof users with less data would be larger, and conversely, thenumber of users with more data would be smaller. As a resultof training and evaluating the models after data distribution,
the accuracy of each model was distributed as shown in Fig. 4,
where many of the models have an accuracy of 10% to 15%,
and these were used as the initial models for the simulations.
D. Simulation Results
The accuracy distribution of each user’s model, given a limitof 40 parameter exchanges, is shown in Fig. 5. The average
accuracy of all models is 45.97% with the gossip method,which is 1.69 times higher than the initial average accuracyof 27.22% in Fig. 4, while the initial maximum accuracy of
61.14% was decreased to 59.04% after simulation. Using the
FedTour algorithm results in an average accuracy of 57.74%
as shown in Fig. 5 which is 2.12 times higher than the initialaverage accuracy. The maximum accuracy of the model is
62.45% which is higher than the initial maximum accuracy.
The average accuracy for different limits of parameter exchange times (L = 20, 30, 40, 50, 70, 100) is shown in Fig. 6.
In both methods, the average accuracy tends to increase as thelimit of parameter exchange times is increased, but overall,
the average accuracy obtained by FedTour is higher than thatof the gossip method. In particular, FedTour outperformed
the gossip method by at least 10%, regardless of the limits.

model that is effective in improving accuracy under a limited
number of parameter exchanges.
In the future, we will conduct simulations under various
conditions, such as changing the number of simulation days
and the number of tourists, to verify the effectiveness of thismethod. We also plan to evaluate our method under real world
situations with more tourism objects included.

160
140

number of model

120
100

gossip protocol
FedTour8060

ACKNOWLEDGMENT
This work was partly supported by JSPS KAKENHI Grant
Number JP21H03431.

40
20
0
10

20

30
40
accuracy[%]

50

60Fig. 5: Accuracy distribution after simulation

Fig. 6: Average accuracy for each communication limit count
For FedTour, when L > 40, the accuracy improvement issaturated. This is due to the difficulty each user had in finding
a peer that would improve their model accuracy. However, thissaturation occurs even before the actual parameter exchanges
reaches the defined limit. For L = 50, the actual parameterexchanges were less than 10.
VI. C ONCLUSION
In this paper we proposed FedTour, a method to construct
tourism object recognition models using tourists’ personal
data. Here, a model update method based on federated learningis adopted to learn the data while protecting the privacy information within the tourist’s data. Specifically, when two user
devices are in range, their model parameters are exchanged,
and updated using FedAvg. To reduce the number of model
parameter exchanges, we proposed a FedTour algorithm thatpredicts the change in accuracy after updating. Model parameters are exchanged only when improved model accuracy
is expected. We conducted a simulation with the proposed
method and the gossip-based method and evaluated it by theaverage of the final model accuracy. Results show that the
average accuracy of the gossip-based method is 45.97% andthat of the proposed method is 57.74%. Since the average
accuracy of the model before the simulation was 27.22%, there
is a significant improvement in accuracy when the proposed
method is applied. We believe that predicting the accuracy
before the actual parameter integration enable us to select a

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