FEDERATED LEARNING METHOD USING SYNONYM

Abstract

A federated learning method using synonym includes: sending a general model to every client device by a moderator; performing a training procedure by each client device, wherein the training procedure includes: encoding private data into a digest; training a client model according to the private data, the digest and the general model, and sending the digest and a client parameter of the client model to the moderator; determining an absent client device of the plurality of client devices by the moderator; generating a synonym of the digest corresponding to the absent client device by a synonym generator; training a replacement model according to the synonym and the digest corresponding to the absent client device by the moderator; and performing an aggregation to update the general model by the moderator according to the replacement model and the client parameter of every client devices except the absent client device.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

This non-provisional application claims priority under 35 U.S.C. § 119(a) on Patent Application No(s). 202210225728.9 filed in China on Mar. 9, 2022, the entire contents of which are hereby incorporated by reference.

BACKGROUND

1. Technical Field

The present disclosure relates to federated learning, and more particularly to a federated learning method using synonym.

2. Related Art

Federated Learning (FL) addresses many privacy and data sharing issues through cross-device and distributed learning via central orchestration. Existing FL methods mostly assume a collaborative setting among clients and can tolerate temporary client disconnection from the moderator.

In practice, however, extended client absence or departure can happen due to business competitions or other non-technical reasons. The performance degradation can be severe when the data are unbalanced, skewed, or non-independent-and-identically-distributed (non-IID) across clients.

Another issue arises when the moderator needs to evaluate and release the model to the consumers. As private client data are not accessible by the moderator, the representative data would be lost when clients cease to collaborate, resulting in largely biased FL gradient update and long-term training degradation. The naive approach of memorizing gradients during training is not a suitable solution, as gradients become unrepresentative very quickly as iteration progresses.

SUMMARY

Accordingly, the present disclosure provides a federated learning method using synonym. This is a FL framework that can address client absence by synthesizing representative client data at the moderator.

According to an embodiment of the present disclosure, a federated learning method using synonym, comprising: sending a general model to each of a plurality of client devices by a moderator; performing a training procedure by each of the plurality of client devices, wherein the training procedure comprises: removing a private portion of private data and encoding the private data into a digest by an encoder; training a client model according to the private data, the digest and the general model; and sending the digest and a client parameter of the client model to the moderator, wherein the client parameter is associated with a weight of the client model; determining an absent client device among the plurality of client devices by the moderator; generating a synonym of the digest corresponding to the absent client device by a synonym generator; training a replacement model according to the synonym and the digest corresponding to the absent client device by the moderator; and performing an aggregation to generate an updated parameter to update the general model by the moderator according to a replacement parameter of the replacement model and the client parameter of each of the plurality of client devices except the absent client device.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure will become more fully understood from the detailed description given hereinbelow and the accompanying drawings which are given by way of illustration only and thus are not limitative of the present disclosure and wherein:

FIG. 1 is a block diagram of the federated learning system using synonym according to an embodiment of the present disclosure;

FIG. 2 is a schematic diagram of the relation of the private data, the digest, and the synonym according to an embodiment of the present disclosure;

FIG. 3 and FIG. 4 are overview diagrams of the federated learning system using synonym according to an embodiment of the present disclosure;

FIG. 5 is an internal architecture diagram of the client model according to an embodiment of the present disclosure;

FIG. 6 is an internal architecture of the replacement model according to an embodiment of the present disclosure;

FIG. 7 is a schematic diagram showing spaces and projections of the private data, digest, synonym, and concatenation of features;

FIG. 8 is a flow chart of the federated learning method using synonym according to a first embodiment of the present disclosure;

FIG. 9 is a detailed flow chart of a step in FIG. 8;

FIG. 10 is a detailed flow chart of a step in FIG. 9;

FIG. 11 is a detailed flow chart of a step in FIG. 8

FIG. 12 is a flow chart of the federated learning method using synonym according to a second embodiment of the present disclosure;

FIG. 13 is a detailed flow chart of a step in FIG. 12; and

FIG. 14, FIG. 15, FIG. 16, and FIG. 17 show the accuracy of the general model in four training scenarios.

DETAILED DESCRIPTION

In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the disclosed embodiments. According to the description, claims and the drawings disclosed in the specification, one skilled in the art may easily understand the concepts and features of the present invention. The following embodiments further illustrate various aspects of the present invention, but are not meant to limit the scope of the present invention.

The detailed description of the embodiments of the present disclosure includes a plurality of technical terms, and the following are the definitions of these technical terms:

Client, the endpoint that contributes the data to join a distributed training or federated learning, also called “client device”.

Moderator, the service provider that collects the models from the clients to aggregate a general model for providing the service.

Private data, the data that are held by a client and need to be protected.

Digest, a sharable representation that can represent the private data. No privacy concerns are included in the digest. The dimension of the digest is usually but not limited to lower than the private data.

Synonym, an alternative representation of private data without privacy concerns. The domains of the synonym and the private data are usually the same.

Client model, the model owned by each client.

Server model, the model owned by the moderator that is aggregated from the client models.

Stochastic Gradient Decent (SGD), an optimization process to update the parameters of a machine learning model based on predefined loss functions.

Federated learning (FL), a collaborative training framework to train a machine learning model without sharing client data to protect the data privacy.

Machine learning, a field of study that gives computers the ability to learn without being explicitly programmed.

Loss function: the objective functions of the optimizing process for training a machine learning model.

The present disclosure proposed a federated learning system using synonym and a federated learning method using synonym.

FIG. 1 is a block diagram of the federated learning system using synonym according to an embodiment of the present disclosure. As shown in FIG. 1, the federated learning system includes a moderator Mo and a plurality of client devices Ci, Cj. The moderator is communicably connected to each of the plurality of client devices Ci, Cj. In an embodiment, one of the following devices may be employed as the moderator Mo or the client devices Ci, Cj: a server, a personal computer, a mobile computing device, and any electronic device for training a machine learning model.

The moderator Mo includes a processor M1, a communication circuit M2, and a storage circuit M3. The processor M1 is electrically connected to the communication circuit M2, and the storage circuit M3 is electrically connected to the processor M1 and the communication circuit M2.

The synonym generator g is configured to generate a synonym according to a digest corresponding to an absent client device. In an embodiment, the synonym generator g is a software running on the processor M1, however, the present disclosure does not limit the hardware configured to execute the synonym generator g. The synonym generator g may be stored in the storage circuit M3 or an internal memory of the processor M1. The detail of the synonym generator g is described later when the encoder ε is described.

The processor M1 is configured to determine the absent client device among the plurality of client devices Ci, Cj. In an embodiment, the processor M1 checks the connection between the communication circuit M2 and each of the plurality of client devices Ci, Cj and thereby determining whether one or more of the client devices Ci, Cj is (are) disconnected and being the absent client device(s). The processor M1 is further configured to initialize a general model, train a replacement model according to the synonym and the digest corresponding to the absent client device, and perform an aggregation to generate an updated parameter to update the general model according to a replacement parameter of the replacement model and the client parameter of each of the plurality of client devices except the absent client device. In an embodiment, the replacement parameter, the client parameter, and the updated parameter are gradients of the neural network model corresponding to these parameters, respectively. Specifically, the replacement parameter is the gradient of the replacement model, the updated parameter is associated with the weights of the client model, such as the gradient of the client model, and the updated parameter is the gradient of the general model. In an embodiment, the aggregation may adopt the FedAvg algorithm. In other embodiments, the aggregation may adopt the FedProx algorithm or the FedNora algorithm.

The federated learning system using synonym (which may be called FedSyn) proposed by the present disclosure aims to jointly train the synonym generator g together with the training of the general model. In an embodiment, the processor M1 is in charge of training the general model and the synonym generator g. In an embodiment, since the synonym generator g can synthesize synonyms from the digests, the synonym generator g should be protected to avoid undesired access from any client devices Ci, Cj to avoid potential data leak or adversarial attacks. For example, the access limitation may be implemented by using the account type or the key of the client device Ci.

The communication circuit M2 is configured to send the general model to each of the plurality of client devices Ci, Cj. The storage circuit M3 is configured to store the digests, the synonyms, the general model, and the replacement model sent from all of the client devices Ci, Cj to the moderator Mo. In an embodiment, the storage circuit M3 is further configured to store the encoder ε.

The hardware architecture of each of the client devices Ci, Cj is basically the same, and the client device Ci in FIG. 1 is used as an example for illustration here. The client device Ci includes a processor i1, a communication circuit i2, and a storage circuit i3. The processor i1 is electrically connected to the encoder ε and the communication circuit i2. The storage circuit i3 is electrically connected to the processor i1 and the communication circuit i2.

The encoder ε is configured to remove a private portion of the private data and encode the private data into the digest. The present disclosure does not limit the type of the private data. For example, the private data is an integrated circuit diagram, and the private portion is a key design in integrated circuit diagram. For example, the private data is a product design layout, and the private portion is the product logo. When the private data is an image, the encoder ε is, for example, an image processing tool providing a function of cropping out the private portion. When the private data is a text recording personal identity information, the encoder ε is configured to convert the original data, such as decreasing the dimension of data or masking specific strings. It should be noticed than the encoder ε should not perturb the data excessively, such as adding excessive noise, and making the data unusable. In an embodiment, the encoder ε proposed by the present disclosure may be implemented by an encoder of the autoencoder. In an embodiment, the dimension of synonym is equal to the dimension of private data. In addition, in an embodiment, the aforementioned communication circuit M2 is further configured to send the encoder ε to each of the plurality of client devices Ci, Cj. In other words, the moderator Mo and each of the plurality of client devices Ci, Cj have the same encoder ε. In an embodiment, the encoder ε is a software running on the processor i1, however, the present disclosure does not limit the hardware configured to execute the encoder ε. The encoder ε may be stored in the storage circuit i3 or an internal memory of the processor i1.

The processor i1 or j1 is configured to train the client model according to the private data, the digest and the general model. In an embodiment, one of the following devices may be employed as the processor i1 or j1: Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Field Programmable Gate Array (FPGA), system-on-a-chip (SoC), and deep learning accelerator. One of the above devices may be employed as the processor M1 of the moderator Mo.

The communication circuit i2 or j2 is configured to send the digest and the client parameter to the moderator Mo. In an embodiment, the communication circuit i2 or j2 may adopt wired network or wireless network. The network type of the communication circuit M2 of the moderator Mo is identical to that of the communication circuit i2 or j2.

The storage circuit i3 or j3 is configured to store the private data, the digest, the general model and the client model. In an embodiment, one of the following devices may be employed as the storage circuit i3 or j3: Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDR SDRAM), flash memory, and hard disk. One of the above devices may be employed as the storage circuit M3 of the moderator Mo.

Please refer to FIG. 2. FIG. 2 is a schematic diagram of the relation of the private data, the digest, and the synonym according to an embodiment of the present disclosure, and uses the client device Ci as an example. As shown in FIG. 2, the encoder ε takes the private data pⁱ of client device Ci as the input and outputs the digest Dⁱ. The synonym generator g takes the digest Dⁱ as the input and outputs the synonym Sⁱ. This way, the digests can be shared or stored outside the client device Ci for FL training. The use of digests and synonyms is versatile and adaptable to most existing architectures to perform FL training in various applications.

FIG. 3 and FIG. 4 are overview diagrams of the federated learning system using synonym according to an embodiment of the present disclosure. FIG. 3 and FIG. 4 represent two different timings in the training process respectively. The timing corresponding to FIG. 4 is later than the timing corresponding to FIG. 3.

The client devices Ci, Cj exist and perform the training tasks respectively at the timing corresponding to FIG. 3.

The encoder ε encodes the private data Pⁱ into the digest Dⁱ, and then sends the digest Dⁱ to the moderator Mo. The client device Ci trains the client model Mⁱ according to the private data Pⁱ, the digest Dⁱ, and the general model M. It should be noted that the client device Ci has already received the general model M from the moderator Mo before the timing corresponding to FIG. 3.

The encoder ε encodes the private data P^j into the digest D^j, and then sends the digest D^j to the moderator Mo. The client device Cj trains the client model M^j according to the private data P^j, the digest D^j, and the general model M. It should be noted that the client device Cj has already received the general model M from the moderator Mo before the timing corresponding to FIG. 3.

The moderator Mo receives the digests Dⁱ, D^j from the client devices Ci, Cj and store thereof. The moderator Mo receives the client parameters of the client models Mⁱ, M^j from the client devices Ci, Cj, and performs the aggregation according to these client parameters to generate the updated parameters for updating the general model M. Finally, the well-trained general model may be deployed on the consumer's device U and being used.

At the timing corresponding to FIG. 4, the client device Ci exists and operates in the same way as described in FIG. 3. However, the client device Cj leaves and becomes the absent client device. To prevent the accuracy of the general model M being affected by the absent client device Cj, the synonym generator g of the moderator Mo generates the synonym S^j according to the digest D^j corresponding to the absent client device Cj, the moderator Mo further trains the replacement model according to the synonym S^j and the digest D^j corresponding to the absent client device Cj, and performs the aggregation to generate the updated parameter to update the general model M according to the replacement parameter of the replacement model and the client parameter of each of the plurality of client devices Ci except the absent client device Cj.

As shown in FIG. 3 and FIG. 4, the above embodiment deals with potential client absence during FL training by encoding the private data Pⁱ, Pⁱ of each of client devices Ci, Cj into digests Dⁱ, D^j. When client device Cj leaves, the moderator Mo generates the synonym S^j to represent the private data P^j from the stored digest to continue training.

Each of the client devices Ci, Cj in the proposed federated learning system using synonym creates digests locally by encoding private training samples, the digests are transmitted and stored at the moderator Mo, so the FL training can continue even if the client devices are absent afterwards.

Please refer to FIG. 4, FIG. 5, and FIG. 6. FIG. 5 is an internal architecture diagram of the client model according to an embodiment of the present disclosure, and takes the client model Mⁱ of the client device Ci as an example. FIG. 6 is an internal architecture of the replacement model according to an embodiment of the present disclosure, and takes the replacement model corresponding to the absent client device Cj as an example.

As shown in FIG. 5, the client model Mⁱ of the client device Ci includes a first feature extractor F_Pⁱ, a second feature extractor F_Dⁱ, and a classifier Cⁱ. The first feature extractor F_Pⁱ takes the private data Pⁱ as the input and outputs a first feature f_Pⁱ. The second feature extractor F_Dⁱ takes the digest Dⁱ as the input and outputs the second feature f_Dⁱ. The classifier Ci takes a concatenation of the first feature f_Pⁱ and the second feature f_Dⁱ as the input and outputs the predicted result {tilde over (y)}_i.

As shown in FIG. 6, the replacement model {circumflex over (M)}_j of the moderator Mo includes a first feature extractor F_P, a second feature extractor F_D, and a classifier C. The first feature extractor F_P takes the synonym S^j as the input and outputs a first feature f_P. The second feature extractor F_D takes the digest D_j as the input and outputs the second feature f_D. The classifier C takes a concatenation of the first feature f_P and the second feature F_D as the input and outputs the predicted result {tilde over (y)}ⁱ.

As shown in FIG. 5 and FIG. 6, the replacement model {circumflex over (M)}^j and the client model Mⁱ have the same structure. This structure includes two feature extractors and one classifier whereas the two models use different data access. The moderator Mo cannot access the private data Pⁱ, but can access the digest Dⁱ. Therefore, the synonym generator g may be used to generate the synonym S^j to continue training.

In general FL, client model training takes places at each client device, and client parameters (e.g. gradient) of the client device are transmitted to the moderator and then aggregated to update the general model. In the federated learning system using synonym, when the client device Ci is available, the client model Mⁱ is trained using the private data Pⁱ together with its digest Dⁱ, as shown in FIG. 5. The client parameters corresponding to the client model are sent to the moderator Mo, the client parameters corresponding to all client devices Ci, Cj are aggregated to generate the updated parameter for updating the general model M, where the client parameters that should be provided by the absent client device (the client device Cj is taken as the example) are provided by the replacement model {circumflex over (M)}^j generated by the moderator Mo, as shown in FIG. 6.

The federated learning system using synonym enforces identical structures for the replacement model {circumflex over (M)}^j and the client model Mⁱ, with one of differences being data accessing. When the client device Cj is available, its private data P^j is used to generate the digest D^j for training. Whenever the client device Cj becomes absent, the moderator Mo may take the digest D^j and reconstruct the synonym S^j for the absent client device Cj to continue training. This way, the training of the federated learning system using synonym is not interrupted whether the client device Cj is present or not.

FIG. 7 is a schematic diagram showing spaces and projections of the private data, digest, synonym, and concatenation of features, and showing the encoder ε, synonym generator g and feature extractors F_D, F_P performing projections in different spaces.

As shown in FIG. 7, the private data Pⁱ and synonym Sⁱ of all client devices Ci form the space A. In all the data in the space A, some data belonging to the same classification form the space A0. The space B is formed by encoding all the data in the space A by the encoder ε, including the private data Pⁱ and synonym Sⁱ, where the private data Pⁱ locates on the client device Ci, the synonym Sⁱ locates on the moderator Mo and is generated by the synonym generator g according to the private data Pⁱ.

The moderator Mo uses the first feature extractor F_P to generate the first feature f_P, uses the second feature extractor F_D to generate the second feature f_D. The concatenation {F_P, F_D} of the first feature F_P and the second feature F_D forms the space F.

As shown in FIG. 7, the client device Ci uses the first feature extractor F_Pⁱ to extract the first feature f_Pⁱ from the private data Pⁱ in the space A and uses the second feature extractor F_Dⁱ to extract the second feature f_Dⁱ from the digest Dⁱ in the space B. The concatenation of these two features is denoted as {f_Pⁱ, f_Dⁱ}. The moderator Mo uses the first feature extractor F_P to extract the first feature f_P from the synonym Sⁱ in the space A0 and uses the second feature extractor F_D to extract the second feature f_D from the digest Dⁱ in the space B. The concatenation of these two features is denoted as {f_P, f_D}. All the concatenations form the space F. In the plurality of concatenations in the space F, those concatenations belonging to the same classification form the space F0. FIG. 7 shows that the client model trained by the client device Ci and the replacement model {circumflex over (M)}ⁱ trained by the moderator Mo generate the same classification results, despite using different training data. In other words, even the moderator Mo cannot obtain the private data Pⁱ of the client device Ci, and the client device Ci becomes the absent client device, the moderator Mo still can train the replacement model {circumflex over (M)}ⁱ according to the synonym Sⁱ and the digest Dⁱ and achieve the same training effect just like owning the private data Pⁱ.

FIG. 8 is a flow chart of the federated learning method using synonym according to a first embodiment of the present disclosure, with steps S1-S6. Step S1 represents “the moderator sends a general model to each of the plurality of client devices”. Step S2 represents “each of the plurality of client devices performs a training procedure”. Step S3 represents “the moderator determines an absent client device among the plurality of client devices”. Step S4 represents “the synonym generator generates a synonym according to the digest corresponding to the absent client device”. Step S5 represents “the moderator trains a replacement model according to the synonym and the digest corresponding to absent client device”. Step S6 represents “the moderator performs an aggregation to generate an updated parameter to update the general model according to a replacement parameter of the replacement model and the client parameter of each of the plurality of client devices except the absent client device”.

The proposed method in an embodiment of the present disclosure may be viewed as an extension of Federated Learning with the newly introduced designs of digests and synonyms together with new loss functions that update the synonym generator and help to retain model performance when trained with possibly absent client devices.

The FL training includes a plurality of iterations and FIG. 8 shows the detail of one of iterations. Please refer to FIG. 1 and FIG. 8. In an embodiment, the method shown in FIG. 8 may adopt the system shown in FIG. 1.

In step S1, the moderator Mo pushes the general model M to each of the plurality of client devices (denoted as “client device Ci” in the following paragraphs).

In an embodiment, step S1 further includes two steps to ensure that every client device Ci has the same encoder ε: the moderator Mo sends the encoder ε to every client device Ci, and the moderator Mo stores the encoder ε. The present disclosure fix the encoder ε to prevent the digest Dⁱ being dependent on the encoder ε, such that the digest Dⁱ is fixed in every training iteration.

Regarding step S2, please refer to FIG. 9. FIG. 9 is a detailed flow chart of step S2 in FIG. 8, with steps S21-S23. Step S21 represents “the encoder removes a private portion of the private data and encodes the private data into a digest”. Step S22 represents “training the client model according to the private data, the digest, and the general model”. Step S23 represents “sending the digest and the client parameter of the client model to the moderator”.

In step S2, as shown in step S21, each client device Ci encodes the private data Pⁱ into the digest Dⁱ. As shown in step S22, the private data Pⁱ and the digest Dⁱ are served as the input data, and the client model Mⁱ is trained by SGD. As shown in step S23, the digest Dⁱ and the client parameter ∇Mⁱ are sent to the moderator Mo, and the digest Dⁱ only needs to be transmitted once at the beginning of training. However, if the private data Pⁱ was updated, the client device Ci has to generate a new digest Dⁱ′ according to the updated private data Pⁱ and sends the new digest Dⁱ′ to the moderator Mo.

Regarding step S21, please refer to FIG. 5 and FIG. 10. FIG. 10 is a detailed flow chart of step S21 in FIG. 9, with steps S211-214. Step S211 represents “inputting the private data to a first feature extractor to generate a first feature”. Step S212 represents “inputting the digest to a second feature extractor to generate a second feature”. Step S213 represents “inputting a concatenation of the first feature and the second feature to a classifier to generate a predicted result”. Step S214 represents “inputting the predicted result and an actual result to a loss function, and adjusting a plurality of weights of at least one of the first feature extractor, the second feature extractor, and the classifier according to an output of the loss function”.

The implementation details of steps S211-S213 may refer to paragraphs regarding aforementioned FIG. 5. In an embodiment of step S214, in each client device Ci, the training data includes of the private data Pⁱ and the digest Dⁱ generated by the encoder ε. Each client device Ci trains the client model Mⁱ using the Client Classification Loss of the standard FedAvg algorithm, as Equation 1 shown below:

L _client =L _CE(Mⁱ(Pⁱ,Dⁱ),y)  (Equation 1)

, where L_CE is the Cross Entropy Loss, Mⁱ(Pⁱ, Dⁱ) denotes the predicted result and y denotes the actual result.

In the process of steps S3-S6, the moderator Mo collects all of client parameters ∇Mⁱ and determines if any client device Ci is absent. If the client device Cj is absent (due to purposely leaving or network congestion), the moderator Mo generates a replacement model {circumflex over (M)}^j to calculate a replacement parameter ∇{circumflex over (M)}^j using the synonym S^j generated from the digest D^j. The moderator Mo updates the general model M by aggregating ∇Mⁱ and ∇{circumflex over (M)}^j.

Please refer to FIG. 5, FIG. 6 and FIG. 11. FIG. 11 is a detailed flow chart of step S5 in FIG. 8, with steps S51-S54. Step S51 represents “inputting the synonym to a first feature extractor to generate a first feature”. Step S52 represents “inputting the digest corresponding to the absent client device to a second feature extractor to generate a second feature”. Step S53 represents “inputting a concatenation of the first feature and the second feature to a classifier to generate a predicted result”. Step S54 represents “inputting the predicted result and an actual result to a loss function, and adjusting a plurality of weights of at least one of the first feature extractor, the second feature extractor, and the classifier”.

As shown in FIG. 5 and FIG. 6, the client model Mⁱ and the replacement model {circumflex over (M)}^j have similar structure while the difference between two models is their input data. Therefore, the implementation details of steps S51-S53 may refer to paragraphs regarding FIG. 6 aforementioned. In an embodiment of step S54, the loss function is identical to the loss function used in step S214.

FIG. 12 is a flow chart of the federated learning method using synonym according to a second embodiment of the present disclosure, where steps S1-S6 are identical to that of FIG. 10. Compared to the first embodiment, the second embodiment further includes step S7, “updating the general model and the synonym generator according to an output of the loss function”.

FIG. 13 is a detailed flow chart of step S7 in FIG. 12, with steps S71-S74. Step S71 represents “inputting the synonym to the encoder of the moderator to generate a digest of synonym”. Step S72 represents “inputting the digest of synonym and the digest corresponding to the absent client device to a first loss function to generate a data similarity loss”. Step S73 represents “inputting predicted data generated by the general model and an actual data to a second loss function to generate a synonym classification loss”. Step S74 represents “computing a weighted sum of the data similarity loss and the synonym classification loss as a moderator loss, and updating the general model and the synonym generator according to the moderator loss”.

In an embodiment of step S71, since the moderator Mo has already stored the encoder ε in step S1, the digest of synonym ε(Sⁱ) may be generated according to the synonym Sⁱ.

In step S72, the Data Similarity Loss L_DSL ensures that the projection of the private data Pⁱ and the projection of the synonym Sⁱ should be similar, as Equation 2 shown below:

L _DSL =L _MSE(ε(S),(D))  (Equation 2)

, where L_MSE is Mean Square Error Loss, S and D denote all synonyms and digests owned by the moderator Mo, respectively. It should be noted that the synonym S is not generated only when there is an absent client device, but all the digests D collected by the moderator Mo will be used to generate corresponding synonyms S.

In step S73, the Synonym Classification Loss LSCL ensures that the synonyms Si and the digests Di should be classified well by the general model M, as Equation 3 shown below:

L _SCL =L _CE(M(Sⁱ,Dⁱ),y)  (Equation 3)

, where L_CE is the Cross Entropy Loss, y denotes the actual result. Since the synonym Sⁱ is generated by the synonym generator g, the convergences of L_SCL and L_DSL are equivalent to the implementation of the training of the synonym generator g.

In step S74, the weighted sum represents the moderator loss L_server for jointly training the synonym generator S and the general model M, as Equation 4 shown below:

L _server =L _DSL +λL _SCL  (Equation 4)

, where λ is a balancing hyperparameter, which is set to λ=1 in an embodiment.

The present disclosure expects the general model M to learn from the synonyms Si generated by the synonym generator g. To achieve this goal, the present disclosure introduces an additional training process at the moderator Mo, as shown by the process of steps S71-S74.

The present disclosure introduces two concepts that advocate joint training of the general model M and the synonym generator g. Specifically, the present disclosure wants the general model M to learn: (1) how to best generate appropriate synonym Sⁱ and (2) how to best perform classification, i.e. determine the predicted result {tilde over (y)}ⁱ from the synonym Sⁱ and the digest Dⁱ. The first concept aims to train the general model M so that it is capable of correctly classifying the information obtained from the digest Dⁱ and the synonym Sⁱ. This concept is enforced by the two sets of arrows F_P and F_D in FIG. 7. The second concept it to make the digest of the synonym ε(Sⁱ) to resemble the digest of the private date Dⁱ. This concept is enforced by the two sets of arrows c in space A and space B in FIG. 7. The above two concepts are implemented by two loss functions proposed in step S72 and step S73.

The following algorithm shows the pseudo code of the federated learning method using synonym according to an embodiment of the present disclosure.

Initialize: M and g
for Each training iteration t (omit t) do
Moderator pushed M to all clients
for client i = 1, 2, ..., n in parallel do
Update Mi with local data Pi with Lclient
Push ∇Mi to the moderator
for absent client j = 1, 2, ..., k in parallel do at the moderator
Update {circumflex over (M)}j with synonyms Sj with Lclient
Moderator aggregates ∇Mi and ∇{circumflex over (M)}j to update M
Moderator updates M and g with loss Lserver,

where M denotes the general model, g denotes the synonym generator, t denotes the number of times of iteration, Mⁱ denotes the client model of the client device Ci, Pⁱ denotes the private data of the client device Ci, L_client denotes the Client Classification Loss, ∇Mⁱ denotes the client parameter (gradient) of the client model Mⁱ, {circumflex over (M)}^j denotes the replacement model, S^j denotes the synonym of the absent client device Cj, ∇{circumflex over (M)}^j denotes the replacement parameter (gradient) of the replacement model {circumflex over (M)}^j, and L_server denotes the moderator loss.

In view of the above, the present disclosure proposes a federated learning method using synonym. This is a FL framework that can address client absence by synthesizing representative client data at the moderator. The present disclosure proposes a data memorizing mechanism to handle the client absence effectively. Specifically, the present disclosure handles the following three scenarios in combinations: (1) unreliable clients, (2) training after removing clients, and (3) training after adding clients.

During the FL training, there are four common training scenarios, (a) a client temporarily leaves during the FL training, (b) a client leaves the training forever, (c) all clients leave the FL training sequentially, and (d) multiple client groups join the FL training in different time slots. Please refer to FIG. 14, FIG. 15, FIG. 16, and FIG. 17. These four figures correspond to the above four scenarios respectively and show the accuracy of the general model, where C0, C1, C2 and C3 represent different client devices. The present disclosure forces the client that contains the largest amount of samples (such as C2) leaving the FL training to highlight the performance impact. As shown by FIGS. 14-17, none of the common FL algorithms, FedAvg, FedNova, FedProx, survive in the target four scenarios on the testing accuracy. On the other hand, the proposed FedSyn achieves a stable testing accuracy on the scenarios. The experiment results show the robustness of FedSyn proposed by the present disclosure.

Claims

1. A federated learning method using synonym, comprising: sending a general model to each of a plurality of client devices by a moderator;performing a training procedure by each of the plurality of client devices, wherein the training procedure comprises: removing a private portion of private data and encoding the private data into a digest by an encoder;training a client model according to the private data, the digest and the general model; andsending the digest and a client parameter of the client model to the moderator, wherein the client parameter is associated with a weight of the client model;determining an absent client device among the plurality of client devices by the moderator;generating a synonym of the digest corresponding to the absent client device by a synonym generator;training a replacement model according to the synonym and the digest corresponding to the absent client device by the moderator; andperforming an aggregation to generate an updated parameter to update the general model by the moderator according to a replacement parameter of the replacement model and the client parameter of each of the plurality of client devices except the absent client device.

2. The federated learning method of claim 1, further comprising: sending the encoder to each of the plurality of the client devices by the moderator; andstoring the encoder by the moderator.

3. The federated learning method of claim 2, further comprising: after updating the general model, performing:inputting the synonym to the encoder of the moderator to generate a digest of synonym;inputting the digest of synonym and the digest corresponding to the absent client device to a first loss function to generate a data similarity loss;inputting predicted data generated by the general model and an actual data to a second loss function to generate a synonym classification loss; andcomputing a weighted sum of the data similarity loss and the synonym classification loss as a moderator loss and updating the general model and the synonym generator according to the moderator loss.

4. The federated learning method of claim 3, wherein the first loss function is mean square error, and the second loss function is cross entropy.

5. The federated learning method of claim 1, wherein training the client model according to the private data, the digest and the general model comprises: inputting the private data to a first feature extractor to generate a first feature;inputting the digest to a second feature extractor to generate a second feature;inputting a concatenation of the first feature and the second feature to a classifier to generate a predicted result; andinputting the predicted result and an actual result to a loss function, and adjusting a plurality of weights of at least one of the first feature extractor, the second feature extractor, and the classifier according to an output of the loss function.

6. The federated learning method of claim 5, wherein the loss function is cross entropy.

7. The federated learning method of claim 1, wherein training the replacement model according to the synonym and the digest corresponding to the absent client device by the moderator comprises: inputting the synonym to a first feature extractor to generate a first feature;inputting the digest corresponding to the absent client device to a second feature extractor to generate a second feature;inputting a concatenation of the first feature and the second feature to a classifier to generate a predicted result; andinputting the predicted result and an actual result to a loss function, and adjusting a plurality of weights of at least one of the first feature extractor, the second feature extractor, and the classifier.

8. The federated learning method of claim 1, wherein a dimension of the synonym equals to a dimension of the private data.

9. The federated learning method of claim 1, wherein training the client model according to the private data, the digest and the general model adopts a stochastic gradient decent method.

10. The federated learning method of claim 1, wherein the aggregation adopts a FedAvg algorithm, a FedProx algorithm, or a FedNora algorithm.