Patent ID: 12236370

DETAILED DESCRIPTION

Hereinafter, embodiments of the present disclosure are described in detail with reference to the accompanying drawings. It should be noted that the same elements will be designated by the same reference numerals although they are shown in different drawings. In the following description, specific details such as detailed configurations and components are merely provided to assist with the overall understanding of the embodiments of the present disclosure. Therefore, it should be apparent to those skilled in the art that various changes and modifications of the embodiments described herein may be made without departing from the scope of the present disclosure. In addition, descriptions of well-known functions and constructions are omitted for clarity and conciseness. The terms described below are terms defined in consideration of the functions in the present disclosure, and may be different according to users, intentions of the users, or customs. Therefore, the definitions of the terms should be determined based on the contents throughout this specification.

The present disclosure may have various modifications and various embodiments, among which embodiments are described below in detail with reference to the accompanying drawings. However, it should be understood that the present disclosure is not limited to the embodiments, but includes all modifications, equivalents, and alternatives within the scope of the present disclosure.

Although the terms including an ordinal number such as first, second, etc. may be used for describing various elements, the structural elements are not restricted by the terms. The terms are only used to distinguish one element from another element. For example, without departing from the scope of the present disclosure, a first structural element may be referred to as a second structural element. Similarly, the second structural element may also be referred to as the first structural element. As used herein, the term “and/or” includes any and all combinations of one or more associated items.

The terms used herein are merely used to describe various embodiments of the present disclosure but are not intended to limit the present disclosure. Singular forms are intended to include plural forms unless the context clearly indicates otherwise. In the present disclosure, it should be understood that the terms “include” or “have” indicate the existence of a feature, a number, a step, an operation, a structural element, parts, or a combination thereof, and do not exclude the existence or probability of the addition of one or more other features, numerals, steps, operations, structural elements, parts, or combinations thereof.

Unless defined differently, all terms used herein have the same meanings as those understood by a person skilled in the art to which the present disclosure belongs. Terms such as those defined in a generally used dictionary are to be interpreted to have the same meanings as the contextual meanings in the relevant field of art, and are not to be interpreted to have ideal or excessively formal meanings unless clearly defined in the present disclosure.

The electronic device according to one embodiment may be one of various types of electronic devices. The electronic devices may include, for example, a portable communication device (e.g., a smart phone), a computer, a portable multimedia device, a portable medical device, a camera, a wearable device, or a home appliance. According to one embodiment of the disclosure, an electronic device is not limited to those described above.

The terms used in the present disclosure are not intended to limit the present disclosure but are intended to include various changes, equivalents, or replacements for a corresponding embodiment. With regard to the descriptions of the accompanying drawings, similar reference numerals may be used to refer to similar or related elements. A singular form of a noun corresponding to an item may include one or more of the things, unless the relevant context clearly indicates otherwise. As used herein, each of such phrases as “A or B,” “at least one of A and B,” “at least one of A or B,” “A, B, or C,” “at least one of A, B, and C,” and “at least one of A, B, or C,” may include all possible combinations of the items enumerated together in a corresponding one of the phrases. As used herein, terms such as “1st,” “2nd,” “first,” and “second” may be used to distinguish a corresponding component from another component, but are not intended to limit the components in other aspects (e.g., importance or order). It is intended that if an element (e.g., a first element) is referred to, with or without the term “operatively” or “communicatively”, as “coupled with,” “coupled to,” “connected with,” or “connected to” another element (e.g., a second element), it indicates that the element may be coupled with the other element directly (e.g., wired), wirelessly, or via a third element.

As used herein, the term “module” may include a unit implemented in hardware, software, or firmware, and may interchangeably be used with other terms, such as, for example, “logic,” “logic block,” “part,” and “circuitry.” A module may be a single integral component, or a minimum unit or part thereof, adapted to perform one or more functions. For example, according to one embodiment, a module may be implemented in a form of an application-specific integrated circuit (ASIC).

Federated learning is a training paradigm that fits a model to a scenario with data in a distributed way.FIG.1is a diagram illustrating a federated learning system, according to an embodiment. A federated server102distributes a global model to client devices104-1to104-n. The client devices104-1to104-nreturn client models with local updates to the federated server, while keeping their local data.

Although the model may maintain an acceptable average performance across client devices, the performance of the model on individual client devices may vary. For example, when collected data is skewed toward a majority group or classes, the model is prone to achieve better performance on data of such classes, compared with data of deficient classes. Accordingly, a federated learning algorithm may result in a model having a high average performance but also a high variance in performance across different client devices.

As used herein, the term “collected data” can refer to data being processed, generally. Collected data need not be data captured via sensors of the device that is doing the processing. Collected data may have already undergone some processing.

Unfairness, or high performance variance, is caused by skewed collected data distribution. Herein, averaging methods are provided that mitigate the bias within the distributed training/collected data in order to train a more fair model. The methods address statistical heterogeneity for federated learning with ZSDG (i.e., data augmentation without explicit sharing of data), and achieve a more fair model across different client devices.

Embodiments of the disclosure provide a federated learning method that employs ZSDG on deficient data distribution to mitigate statistical heterogeneity. This encourages more uniform performance accuracy across client devices in federated networks. Such methods improve accuracy and fairness simultaneously.

A first method is provided to improve the fairness of federated learning via FMIL, in which client devices (e.g., local nodes) perform model inversion on a global model that is delivered from a federated server. Accordingly, data is derived at a client device even though training data of other client devices was not shared.

A second method is provided to improve the fairness of federated learning via LMIF, in which the federated server performs model inversion on received client models to generate synthetic data, and thereby, a balanced dataset to be used in an extra training step at the federated server. Accordingly, data is derived at the server even though training data of client devices was not shared.

In accordance with the second method, the client devices may also be trained to match the layer statistics of the federated server, assuming the federated server already provides a fair model, using, for example, LMIF. This prevents the client devices from being too biased towards their own data.

In accordance with both the first and second methods, model inversion without data is performed by generating fake data, or synthetic data, from a trained model without accessing actual training data. Each data sample has a class label.

In a first embodiment of model inversion, model M is set as a neural network containing L layers. For simplicity, it is assumed that the model M has L batch normalization (BN) layers, and activations before the i-th BN layer are denoted as zi.

During forward propagation, ziis normalized by mean μiand variance σiparameterized in i-th BN layer. Note that given a pre-trained model M, BN statistics of all BN layers are stored and accessible. Therefore, in order to generate fake input {tilde over (x)} that best matches the BN statistics stored in the BN layers and given target classy, an optimization problem can be solved as set forth in Equation (2) below.

minx-∑i=1Lμ_ir-μi22+σ_ir-σi22+L⁡(M⁡(x_),y_)(2)

In order to solve Equation (2), the model parameters are fixed and the input from a random sample is updated by gradient descent. Since the input reconstruction requires no training data, it is referred to as ZSDG. Since the pre-trained model M is fixed, the visual quality of the generated fake input {tilde over (x)} is highly dependent on the performance of M.

In another embodiment of model inversion, a generator is trained to generate synthetic data using data-free adversarial knowledge distillation. The generator is trained with adversarial losses to force the generator to produce synthetic data, which performs similarly to collected data (e.g., real data or original data) for knowledge distillation from a reference network (teacher) to another network (student). The generator is trained to force its synthetic data to produce statistics that match the BN statistics stored in a reference model pre-trained with collected data. The generator is also trained such that its synthetic images produce small categorical entropy and high batch categorical entropy from the reference pre-trained network. A conditional generator may be used to generate data corresponding to a specific label.

According to an embodiment provided to improve the fairness of federated learning via FMIL, client devices (e.g., local nodes) derive data by inverting the model delivered by the federated server. This generated data is used for data-augmentation to improve training of local client models.

Herein, (x; y) is set as a real local training input/label pair and (x;y) is set as generated synthetic data (e.g., fake data). To mitigate statistical heterogeneity, each client device augments its training set by synthesizing data for classes having deficient collected data.

Specifically, the server distributes the model M to each client device, and the client device performs ZSDG to generatexby iterating all possibley, enabling local training to be based on a more balanced dataset. For example, if (xi, yi) is the data for the i-th client device, and (xi;yi) is the synthetic data generated at the i-th client device, then the i-th client device will use augmented data [(xi, yi); (xi;yi)]. After local training, the client device returns the updated model M back to the server for aggregation.

Each client device has its own set of training/collected data (xi, yi) that has different statistical properties, and the synthetic data generated at each client (xi;yi) is not the same due to randomness in image generation in the model-inversion process.

FIG.2is a flowchart illustrating a method for FMIL using ZSDG, according to an embodiment. For simplicity in description, the label y, corresponding to the data element x, is removed.

At202, at communication round t, a federated server distributes a federated global model Mf,t-1to a subset of client devices St. At204, each client device i in the subset of client devices Stgenerates synthetic data {tilde over (x)}ifor all of the classes in the federated global model Mf, or for a subset of classes in which collected data is deficient, by performing model inversion on the federated global model (e.g., by using image distillation or ZSDG). At206, each client i augments its collected data xiwith the synthetic data {tilde over (x)}iin order to generate an augmented dataset {xi, {tilde over (x)}i}tthat has a more uniform distribution than the collected data xiacross the classes of the federated global model Mf.

Generally, in discerning whether a dataset has a more uniform distribution across classes, a frequency distribution of classes in the dataset is used as a metric. If the class distribution approaches a discrete uniform distribution, the dataset is more uniform. For example, if the size of the dataset (e.g., the number of labeled dataset points) is S, and there are N classes, the dataset is uniform if there are S/N labeled points for each of the N classes.

At208, each client device i trains the federated global model Mf,t-1on its augmented dataset, and generates an updated client model Mci,t. At210, each client i transmits the updated client model Mci,tto the federated server. At212, the federated server averages the client models received from the subset of client devices Stusing a weighted average Mf,t=ΣiwiMci,t, and generates a new federated average model that may be redistributed to the client devices, returning the methodology to202.

In testing the method relating to FMIL, a server may select a fraction C=0:1 of 100 clients during each communication round, with T=100 total rounds. Each selected client may train its own model for E=5 local epochs with mini-batch size B=10. Each client has at most 2 classes of images and each class contains 250 images. Starting at the 80th round, ZSDG may be launched for local training. The amount of each augmented class is 64. After data augmented federated learning, the final aggregated model is tested and the test accuracy and variance across all clients is obtained. The final aggregated model of the FMIL method has a higher test accuracy and less variance (more fairness) compared to the standard federated average model. Specifically, performance and fairness are simultaneously improved.

The method relating to FMIL may be appropriately utilized when client devices have sufficient computational and storage complexity, such as, for example, when a client is a hospital having no access to data from other hospitals, but an interest to learn from such data.

According to an embodiment provided to improve the fairness of federated learning via LMIF, models that are delivered from local nodes are inverted at the federated server to derive synthetic data. This generated synthetic data is used by the federated server to improve the combining of the local client models, or the model averaging process.

Constraints on local client devices include limited computing resources and storage capacity, which may restrict the application of data augmentation described above with respect to FMIL. Additionally, the i-th client device may be more concerned with model performance on its own collected data (xi, yi), rather than a general model that works for all data. Consequently, in this embodiment, ZSDG and federated learning is proposed on the federated server end.

Specifically, the federated server distributes the federated global (averaged) model M to each client device. Each client device i trains the federated global model M using its collected local data (xi, yi). Each client device i returns an updated model Miback to the federated server. The server performs ZSDG for each updated model Miin order to generate synthetic data for each client i, and mixes the synthetic data in a balanced way. The server aggregates the newly received models into an updated averaged federated model using an averaging algorithm. The server fine-tunes the weights of the averaged model in order to generate a fair federated model via training with the mixed synthetic data.

According to another embodiment, the server runs the averaging algorithm on the received client models to generate a first averaged federated model. The server generates synthetic data (e.g., fake data) from the first averaged federated model. The server evaluates the first averaged federated model based on the generated synthetic data, and determines deficient classes. The server generates more data from its first averaged federated model for the deficient classes, and mixes the additionally generated synthetic data with the previous data, generating a dataset having more samples in the deficient classes. The server fine-tunes its first averaged model using the newly generated data.

Compared to data augmentation on client device end (FMIL), augmentation on the server end (LMIF) avoids computing and storage limits on local devices. In addition, each client device updates the received model from a same initialization point, encouraging fair model performance as a whole.

FIG.3is a flowchart illustrating a method for LMIF using ZSDG, according to an embodiment.

At302, at communication round t, the federated server distributes a federated global model Mf,t-1to a subset of clients St. At304, each client i in the subset of clients St, trains the federated global model Mf,t-1on its true data {xi}t, and generates an updated client model Mci,t. At306, each client i transmits its updated client model Mci,tto the federated server.

At308, the federated server performs model inversion on each received client model Mci,tand generates synthetic data {{tilde over (x)}i}tin order to generate a balanced synthetic federated dataset {}twith a uniform distribution across the classes of the federated global model Mf. In another embodiment, the server may generate a skewed dataset to compensate for deficient classes if the server has prior knowledge of client data distributions.

As described above, a dataset has a more uniform distribution across classes, if the class distribution approaches a discrete uniform distribution. For example, if the size of the dataset (e.g., the number of labeled dataset points) is S, and there are N classes, the dataset is uniform if there are S/N labeled points for each of the N classes.

At310, the federated server averages the received client models using a weighted average {tilde over (M)}f,t=τiwiMci,t. In another embodiment, the federated server generates more data from its first averaged federated model {tilde over (M)}f,tfor the deficient classes form {}tso that the dataset has more samples of the deficient classes. In yet another embodiment, the federated server augments its dataset with data generated from previous federated training epochs {}t=Uτ=1t{}τ.

At312, the server trains or fine-tunes the averaged federated model {tilde over (M)}f,ton the balanced synthetic federated dataset {}tin order to generate an updated federated model Mf,t. This updated federated model may then be redistributed to the client devices by returning to302.

The embodiments ofFIG.1(LMIF) andFIG.2(FMIL) may be performed concurrently, in the same federated learning epoch, or in alternating federated learning epochs.

When a fair model is generated on the server end, the training algorithm at the local client devices (e.g., local nodes) may be changed, and the training may be regularized such that the statistics of model weights at client devices are close to that of the federated server, assuming the federated server model is better or more fair, thereby encouraging a fair local model. In an embodiment, stored BN statistics of the federated model are used. In another embodiment, statistics of the lthlayer of the kthclient model and the federated model f are matched for the input data at each training batch.

The weights of the kthuser at training epoch (t+1) are updated to minimize an objective function where Fk(⋅) is the main training objective μk,l, μf,lare the mean of the lthlayer (or that stored in its corresponding batch normalization layer) of the kthuser and the federated model, respectively. Similarly, σk,l, σf,lare the standard deviations of the lthlayer or those of its corresponding BN layer of the kthuser and the federated model, respectively. The objective function is set forth in Equation (3) below.

wk,t+1=argminwFk(w)+α⁢∑lμk,l-μf,l2+β⁢∑lσk,l-σf,l2(3)

Accordingly, client devices match the statistics at the federated model, and it is implicitly assumed that the federated model is fair. This training procedure may be deployed at the client devices together with the LMIF at the federated server, which ensures that the federated model that is distributed to clients is fair.

In another embodiment, instead of minimizing the distances between the mean and variances, as described above, the distance between the distributions is minimized by minimizing the KL divergence between two Gaussian distributions with the corresponding mean and variances set forth in Equation (4) below.

KL[N⁡(μk,l,σk,l2),N⁡(μf,l,σf,l2)]=(μk,l-μf,l)2+σk,l22⁢σf,l2-log⁢σk,lσf,l-12,(4)

FIG.4is a block diagram of an electronic device in a network environment, according to one embodiment. With respect to the embodiment ofFIG.2, an electronic device401ofFIG.4may be embodied as the client device and a server408ofFIG.4may be embodied as the federated server. With respect to the embodiment ofFIG.3, the electronic device401may be embodied as the federated server and external electronic devices402and404ofFIG.4may be embodied as the client devices.

Referring toFIG.4, the electronic device401in a network environment400may communicate with the electronic device402via a first network498(e.g., a short-range wireless communication network), or the electronic device404or the server408via a second network499(e.g., a long-range wireless communication network). The electronic device401may communicate with the electronic device404via the server408. The electronic device401may include a processor420, a memory430, an input device450, a sound output device455, a display device460, an audio module470, a sensor module476, an interface477, a haptic module479, a camera module480, a power management module488, a battery489, a communication module490, a subscriber identification module (SIM)496, or an antenna module497. In one embodiment, at least one (e.g., the display device460or the camera module480) of the components may be omitted from the electronic device401, or one or more other components may be added to the electronic device401. Some of the components may be implemented as a single integrated circuit (IC). For example, the sensor module476(e.g., a fingerprint sensor, an iris sensor, or an illuminance sensor) may be embedded in the display device460(e.g., a display).

The processor420may execute, for example, software (e.g., a program440) to control at least one other component (e.g., a hardware or a software component) of the electronic device401coupled with the processor420, and may perform various data processing or computations. As at least part of the data processing or computations, the processor420may load a command or data received from another component (e.g., the sensor module476or the communication module490) in volatile memory432, process the command or the data stored in the volatile memory432, and store resulting data in non-volatile memory434. The processor420may include a main processor421(e.g., a central processing unit (CPU) or an application processor (AP)), and an auxiliary processor423(e.g., a graphics processing unit (GPU), an image signal processor (ISP), a sensor hub processor, or a communication processor (CP)) that is operable independently from, or in conjunction with, the main processor421. Additionally or alternatively, the auxiliary processor423may be adapted to consume less power than the main processor421, or execute a particular function. The auxiliary processor423may be implemented as being separate from, or a part of, the main processor421.

The auxiliary processor423may control at least some of the functions or states related to at least one component (e.g., the display device460, the sensor module476, or the communication module490) among the components of the electronic device401, instead of the main processor421while the main processor421is in an inactive (e.g., sleep) state, or together with the main processor421while the main processor421is in an active state (e.g., executing an application). The auxiliary processor423(e.g., an image signal processor or a communication processor) may be implemented as part of another component (e.g., the camera module480or the communication module490) functionally related to the auxiliary processor423.

The memory430may store various data used by at least one component (e.g., the processor420or the sensor module476) of the electronic device401. The various data may include, for example, software (e.g., the program440) and input data or output data for a command related thereto. The memory430may include the volatile memory432or the non-volatile memory434.

The program440may be stored in the memory430as software, and may include, for example, an operating system (OS)442, middleware444, or an application446.

The input device450may receive a command or data to be used by another component (e.g., the processor420) of the electronic device401, from the outside (e.g., a user) of the electronic device401. The input device450may include, for example, a microphone, a mouse, or a keyboard.

The sound output device455may output sound signals to the outside of the electronic device401. The sound output device455may include, for example, a speaker or a receiver. The speaker may be used for general purposes, such as playing multimedia or recording, and the receiver may be used for receiving an incoming call. The receiver may be implemented as being separate from, or a part of, the speaker.

The display device460may visually provide information to the outside (e.g., a user) of the electronic device401. The display device460may include, for example, a display, a hologram device, or a projector and control circuitry to control a corresponding one of the display, hologram device, and projector. The display device460may include touch circuitry adapted to detect a touch, or sensor circuitry (e.g., a pressure sensor) adapted to measure the intensity of force incurred by the touch.

The audio module470may convert a sound into an electrical signal and vice versa. The audio module470may obtain the sound via the input device450, or output the sound via the sound output device455or a headphone of an external electronic device402directly (e.g., wired) or wirelessly coupled with the electronic device401.

The sensor module476may detect an operational state (e.g., power or temperature) of the electronic device401or an environmental state (e.g., a state of a user) external to the electronic device401, and then generate an electrical signal or data value corresponding to the detected state. The sensor module476may include, for example, a gesture sensor, a gyro sensor, an atmospheric pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an infrared (IR) sensor, a biometric sensor, a temperature sensor, a humidity sensor, or an illuminance sensor.

The interface477may support one or more specified protocols to be used for the electronic device401to be coupled with the external electronic device402directly (e.g., wired) or wirelessly. The interface477may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, a secure digital (SD) card interface, or an audio interface.

A connecting terminal478may include a connector via which the electronic device401may be physically connected with the external electronic device402. The connecting terminal478may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (e.g., a headphone connector).

The haptic module479may convert an electrical signal into a mechanical stimulus (e.g., a vibration or a movement) or an electrical stimulus which may be recognized by a user via tactile sensation or kinesthetic sensation. The haptic module479may include, for example, a motor, a piezoelectric element, or an electrical stimulator.

The camera module480may capture a still image or moving images. The camera module480may include one or more lenses, image sensors, image signal processors, or flashes.

The power management module488may manage power supplied to the electronic device401. The power management module488may be implemented as at least part of, for example, a power management integrated circuit (PMIC).

The battery489may supply power to at least one component of the electronic device401. The battery489may include, for example, a primary cell which is not rechargeable, a secondary cell which is rechargeable, or a fuel cell.

The communication module490may support establishing a direct (e.g., wired) communication channel or a wireless communication channel between the electronic device401and the external electronic device (e.g., the electronic device402, the electronic device404, or the server408) and performing communication via the established communication channel. The communication module490may include one or more communication processors that are operable independently from the processor420(e.g., the AP) and supports a direct (e.g., wired) communication or a wireless communication. The communication module490may include a wireless communication module492(e.g., a cellular communication module, a short-range wireless communication module, or a global navigation satellite system (GNSS) communication module) or a wired communication module494(e.g., a local area network (LAN) communication module or a power line communication (PLC) module). A corresponding one of these communication modules may communicate with the external electronic device via the first network498(e.g., a short-range communication network, such as Bluetooth™, wireless-fidelity (Wi-Fi) direct, or a standard of the Infrared Data Association (IrDA)) or the second network499(e.g., a long-range communication network, such as a cellular network, the Internet, or a computer network (e.g., LAN or wide area network (WAN)). These various types of communication modules may be implemented as a single component (e.g., a single IC), or may be implemented as multiple components (e.g., multiple ICs) that are separate from each other. The wireless communication module492may identify and authenticate the electronic device401in a communication network, such as the first network498or the second network499, using subscriber information (e.g., international mobile subscriber identity (IMSI)) stored in the subscriber identification module496.

The antenna module497may transmit or receive a signal or power to or from the outside (e.g., the external electronic device) of the electronic device401. The antenna module497may include one or more antennas, and, therefrom, at least one antenna appropriate for a communication scheme used in the communication network, such as the first network498or the second network499, may be selected, for example, by the communication module490(e.g., the wireless communication module492). The signal or the power may then be transmitted or received between the communication module490and the external electronic device via the selected at least one antenna.

At least some of the above-described components may be mutually coupled and communicate signals (e.g., commands or data) therebetween via an inter-peripheral communication scheme (e.g., a bus, a general purpose input and output (GPIO), a serial peripheral interface (SPI), or a mobile industry processor interface (MIPI)).

Commands or data may be transmitted or received between the electronic device401and the external electronic device404via the server408coupled with the second network499. Each of the electronic devices402and404may be a device of a same type as, or a different type, from the electronic device401. All or some of operations to be executed at the electronic device401may be executed at one or more of the external electronic devices402,404, or408. For example, if the electronic device401should perform a function or a service automatically, or in response to a request from a user or another device, the electronic device401, instead of, or in addition to, executing the function or the service, may request the one or more external electronic devices to perform at least part of the function or the service. The one or more external electronic devices receiving the request may perform the at least part of the function or the service requested, or an additional function or an additional service related to the request, and transfer an outcome of the performing to the electronic device401. The electronic device401may provide the outcome, with or without further processing of the outcome, as at least part of a reply to the request. To that end, a cloud computing, distributed computing, or client-server computing technology may be used, for example.

One embodiment may be implemented as software (e.g., the program440) including one or more instructions that are stored in a storage medium (e.g., internal memory436or external memory438) that is readable by a machine (e.g., the electronic device401). For example, a processor of the electronic device401may invoke at least one of the one or more instructions stored in the storage medium, and execute it, with or without using one or more other components under the control of the processor. Thus, a machine may be operated to perform at least one function according to the at least one instruction invoked. The one or more instructions may include code generated by a complier or code executable by an interpreter. A machine-readable storage medium may be provided in the form of a non-transitory storage medium. The term “non-transitory” indicates that the storage medium is a tangible device, and does not include a signal (e.g., an electromagnetic wave), but this term does not differentiate between where data is semi-permanently stored in the storage medium and where the data is temporarily stored in the storage medium.

According to one embodiment, a method of the disclosure may be included and provided in a computer program product. The computer program product may be traded as a product between a seller and a buyer. The computer program product may be distributed in the form of a machine-readable storage medium (e.g., a compact disc read only memory (CD-ROM)), or be distributed (e.g., downloaded or uploaded) online via an application store (e.g., Play Store™), or between two user devices (e.g., smart phones) directly. If distributed online, at least part of the computer program product may be temporarily generated or at least temporarily stored in the machine-readable storage medium, such as memory of the manufacturer's server, a server of the application store, or a relay server.

According to one embodiment, each component (e.g., a module or a program) of the above-described components may include a single entity or multiple entities. One or more of the above-described components may be omitted, or one or more other components may be added. Alternatively or additionally, a plurality of components (e.g., modules or programs) may be integrated into a single component. In this case, the integrated component may still perform one or more functions of each of the plurality of components in the same or similar manner as they are performed by a corresponding one of the plurality of components before the integration. Operations performed by the module, the program, or another component may be carried out sequentially, in parallel, repeatedly, or heuristically, or one or more of the operations may be executed in a different order or omitted, or one or more other operations may be added.

Although certain embodiments of the present disclosure have been described in the detailed description of the present disclosure, the present disclosure may be modified in various forms without departing from the scope of the present disclosure. Thus, the scope of the present disclosure shall not be determined merely based on the described embodiments, but rather determined based on the accompanying claims and equivalents thereto.