Artificial intelligence (AI) has been applied in phishing email detection. Typically, it requires rich email data from a collection of sources, and the data usually contains private information that needs to be preserved. So far, AI techniques are solely focusing on centralized data training that eventually accesses sensitive raw email data from the collected data repository. Thus, a privacy-friendly AI technique such as federated learning (FL) is a desideratum. FL enables learning over distributed email datasets to protect their privacy without the requirement of accessing them during the learning in a distributed computing framework. This work, to the best of our knowledge, is the first to investigate the applicability of training email anti-phishing model via FL. Building upon the deep neural network model, in particular, Recurrent Convolutional Neural Network for phishing email detection, we comprehensively measure and evaluate the FL-entangled learning performance under various settings, including balanced and imbalanced data distribution among clients, scalability, communication overhead, and transfer learning. Our results positively corroborate comparable performance statistics of FL in phishing email detection to centralized learning. As a trade-off to privacy and distributed learning, FL has a communication overhead of 0.179 GB per global epoch per its clients. Our measurementbased results find that FL is suitable for practical scenarios, where data size variation, including the ratio of phishing to legitimate email samples, among the clients, are present. In all these scenarios, FL shows a similar performance of testing accuracy of around 98%. Besides, we demonstrate the integration of the newly joined clients with time in FL via transfer learning to improve the client-level performance. The transfer learning-enabled training results in the improvement of the testing accuracy by up to 2.6% and fast convergence.
Email is the most usual means of formal communication. At the same time, it is exploited as a common tool for phishing attacks, where attackers disguise as a trustworthy entity and try to install malware or obtain sensitive information such as login credentials and bank details of the recipient. Based on the phishing and email fraud statistics 2019 [40] , phishing accounts for 90% of data breaches, which leads to an average financial loss of $3.86 million. Moreover, phishing attacks cost American business half a billion dollars a year [28] , and it is increasing. Recently, COVID-19 drives phishing emails up to an unprecedented level by over 600% [32] .
Correspondingly, there are various techniques devised to protect users from phishing attacks. These techniques can be generally divided into two categories, namely traditional methods, and artificial intelligence (AI) based methods. The traditional method is verification based, where emails are filtered out by comparing with references, specifically, known email formats, which relies on either a list of phishing emails (blacklist), or a list of legitimate emails (whitelist), or the contents of the known phishing emails. However, email formats can be easily manipulated with time by the attackers, which renders traditional method inefficient. AI-based methods learn to classify an email as phishing or legitimate with a high probability (e.g., 0.99848 [11] ). This method is context aware, and it can continuously learn from the newly available email data samples and adapts to handle the new attack formats/cases efficiently on time.
AI-based methods can be further broadly classified into two parts, namely conventional ML-based and deep learning (DL) based methods. The performance of a conventional ML-based method depends on delicate feature selection (e.g., semantic and syntax) and processing. Such feature engineering usually requires domain knowledge and trials, so that it is time-consuming and laborious, which hinders improvements against evolving threats. Moreover, it is hard to capture full contextual information of email data. Naive Bayes, Support Vector Machine, and Decision Tree are examples of conventional ML-based methods [8] . On the contrary, a DL-based method feeds the input directly to the system, and it extracts the critical features and the contextual information by itself. This contributes to high efficiency as well as better performance. Convolutional Neural Network [26] and Recurrent Convolutional Neural Network [11] are typical examples of the DL-based method. Although DL-based methods are preferable over other methods considering its performance and automated feature engineering, as a trade-off, it requires a considerable amount of email data.
Unfortunately, emails are sensitive to clients, and disclosure to third parties is not preferred. Thus, the organisations or companies are reluctant to share their email data for the improvement of the anti-phishing DL model. Even anonymization of the email is problematic because it can be easily circumvented -attackers can exploit various characteristics, e.g., social graphs to re-identify the victim's entity [14] . As such, it is non-trivial to aggregate emails for centralized analysis. Besides, a recent work [20] emphasizes the strict ethical concerns when accessing and analyzing the emails of 92 organizations even with the access permission. Along with access control, strict rules are required to be followed: Firstly, the emails are encrypted during fetching; secondly, only authorized employees at the email analyzing agent can access the data (under the standard, strict access control policies); thirdly, personally identifying information or sensitive data disclosed to the authorized employees must not be shared with others; once the model is built, all the encrypted email must be deleted [7] . For any purpose, improperly centralized data management could violate specific rules such as reusing the data indiscriminately and risk-agnostic data processing [42] required by General Data Protection Regulation (GDPR) [10] and HIPAA [3] . Therefore, even with the users' permission to use their data for agreed tasks (e.g., DL), handling the email data under a centralized cloud is still risky under the set of privacy regulations. Overall, there is an urgent need to process a DL-based method without accessing the raw email data for anti-phishing purposes.
Recently, collaborative learning among a large number of participants has become popular, where a joint DL model can be trained by harvesting the rich distributed data held by each participant without accessing them. One most popular technique is federated learning (FL) [6, 21, 29] . To the best of our knowledge, the applicability of FL for email anti-phishing has not been explicitly investigated. In this paper, we take the first empirical measurement of email anti-phishing performance by leveraging FL.
This work examines the following five research questions that aim to capture the practical scenarios and challenges. Figure 1 : An overview of federated learning.
Federated learning [29] allows parallel DL training across distributed clients, and pushes the computation to the edge devices (i.e., clients). Figure 1 illustrates an overview of FL. There are four exemplified clients with their local email datasets and one coordinating server. Firstly, each client i trains the model on their local email datasets D i and produce the local model W i t at time instance t, for i ∈ {1, 2, 3, 4}. Secondly, all clients upload their local models to the server. Then the server performs the weighted averaging (i.e., aggregation) of the local models and updates the global model W t +1 . Finally, the global model is broadcast to all clients, and this completes the one round of FL process. This process continues until the model converges. In FL, the server synchronizes the training process across the clients. Over the entire training process, only the models (i.e., model parameters) are transmitted between the clients and the server. Thus, a client (e.g., financial institution) does not require to share their raw email data to the server (e.g., coordinated by an email analyzer) during the training process. Thus, the data are always local and confidential that makes FL a privacy-preserving technique. Transfer learning [38] utilizes the pre-trained ML model in the related dataset to the current dataset. It provides a faster convergence and good performance in the current dataset due to the transfer of the previous knowledge in the related dataset. Figure 2 illustrates an example overview of transfer learning in the similar dataset. Firstly, at time t, model W t trains on the email dataset D 1 with a performance (e.g., accuracy) of P 11 . Secondly, at time t ′ , W t trains on the email dataset D 2 and evolves toW t ′ , which has a performance of P 22 on D 2 , and P 21 on D 1 . The transfer learning focuses on the improvement of the P 22 on D 2 , and does not care about P 21 on D 1 . It is applicable both in CL and FL settings, and useful to train the model even with fewer data samples.
D 1 W t D 2 W t' t t' P 11 P 22 P 21
In this work, phishing and legitimate email samples are collected from three popular sources, namely First Security and Privacy Analytics Anti-Phishing Shared Task (IWSPA-AP) [23] , NazarioâĂŹs phishing corpora(Nazario) [33] , and Enron Email Dataset (Enron) [39] . The dataset contains email samples with both header 1 Figure 3 : An overview of THEMIS model.
created by Data engine [35] , Enron (emails generated by employees of Enron corporation), and Nazario (personal collection). To provide more insight into the email samples, we present some frequently appeared words in them as follows:
• IWSPA-AP phishing email (a) with header includes account, PayPal, please, eBay, link, security, update, bank, online, and information, and (b) without header includes text, account, email, please, information, click, team, online, and security. IWSPA-AP legitimate email (a) with header includes email, please, new, sent, party, people, Donald, state, and president, and (b) without header includes text, link, national, US, Trump, and democratic. • Nazario includes important, account, update, please, email, security, PayPal, eBay, bank, access, information, item, click, confirm, and service. • Enron includes text, plain, subject, please, email, power, image, time, know, this, message, information, and energy. We have considered the updated email dataset from the sources till this date, e.g., Nazario's phishing corpus 2019. In total, the experimental dataset has 23475 email samples, and Table 1 shows the number of emails extracted from each source. 
THEMIS is one of the recent models, which has been demonstrated to be highly effective for phishing email detection. It employs Recurrent Convolutional Neural Network (RCNNs) and models emails at multiple levels, including char-level email header, word-level email header, char-level email body, and word-level email body [11] . This way, it captures the deep underlying semantics of the phishing emails efficiently and consequently making THEMIS better than existing DL-based methods, that are limited to the natural language processing and deep learning [27] .
Model Overview: Fig. 3 illustrates a system overview of the THEMIS model. Firstly, THEMIS extracts the charlevel and word-level of email header and body, and then an embedding layer converts all these levels to the respective vector representation. Afterward, it feeds each vector representation into RCNN model [24] and learns a representation for the email header and email body, respectively. THEMIS RCNN consists of four Bidirectional-Long Short-Term Memory (Bi-LSTM) that obtain the left and right semantic information of a specific location with its embedding information from the above four vectors, thus forming something called a triple. Next, these triples are mapped into specified dimensions using a tanh activation function. The longitudinal max polling is then applied to obtain four different representations, which will be paired to form only two representations for the header and the body. As the email header representation and body representation have varying degrees of impact on phishing detection, an attention mechanism is applied to compute a weighted sum of the two representations, and this produces an ultimate representation of the whole email, which is further processed to produce the classification result. For more details of the THEMIS model, we refer readers to [11] .
It is reported that THEMIS can achieve up to 99.848% of overall testing accuracy. Considering its high efficacy in phishing detection, we thus chose this model as a CL baseline across all our FL-based experiment settings.
The email dataset has two types of file formats, viz text file (.txt), and mbox file (.mbox). Each email is a single text file if the email sample is in the text format. In the mbox format, all messages are concatenated and stored as plain text in a single file. Moreover, each message starts with the four characters "From" followed by a space. Both types of the email files are firstly parsed into two parts, namely email header and email body, and then subjected to further processing, including cleaning and tokenization to produce char level and word level sequences (described in the following paragraph). By considering equal phishing and legitimate email samples from the total dataset (of 23475 data samples), we prepare the experimental dataset of size 20044 (i.e., 2 × 10022) -to be precise, 10022 is aligned with the number of phishing emails while the number of legitimate emails is 13453. Moreover, the new dataset has four parts -phishing header, phishing body, legitimate header, and legitimate body -each part with 10022 samples. The experimental dataset is equally and uniformly distributed in all our distributed setup with multiple clients except for the cases with the imbalanced dataset (where the number of phishing and legitimate email samples varies with the case). For example, cases with five clients have a dataset of size 4008 (i.e., around 20044 divided by 5) in each client. For all experiments, the training-to-testing data split ratio is 80:20.
3.3.1 Extraction of Header and Body. The class Header of the python module, called email.header [12] , is used to extract the email header, and this separates the header and body part of the email samples. In the header section, we consider only the Subject and the Content-Type field, which are deemed essential for phishing detection. This separation is done by using the python library called the regular expression (RE) module [22].
Body. The python library Beautiful Soup 4 [17] and HTML parser [36] are used to clean the text information in HTML format. Besides, we use RE for the plain text (both in header and body) cleaning by removing punctuation and non-alphabetic characters. To filter out the stop words from the header and body, we use stopwords of the nltk packages (nltk.corpus) [5] of python.
To get the char-level and word-level sequences of the tokens for both header and body parts, the Tokenizer class provided by Keras library [37] is used. Basically, this is to encode each character/word as a unique integer as required by the input format of the embedding layer. Two main functions are used for tokenization; these are âĂŸ-fit_on_texts,âĂŹ which updates internal vocabulary based on a list of texts, and âĂŸtexts_to_sequences,âĂŹ which transforms each text in texts to a sequence of integers by considering only words known by the tokenizer. In all our measurements, we keep 50, 100, 150, and 300 as the length of the four sequences of tokens, which are word-level header, char-level header, word-level body, and char-level body, respectively.
For the purpose of performance measurements, we use Highperformance Computing (HPC) platform that is built on Dell EMC's PowerEdge platform. It has the Tesla P100-SXM2-16GB GPU model. All code is written in Python 3.6.1, and the THEMIS model, that has a RCNN, is implemented by using TensorFlow 2.2.5 [1] and Keras 2.2.5 [44] framework. In all measurements, we keep the same random seed, i.e., random.seed(123). We run centralized model training and federated model training under various settings in our experiments, but with the same learning rate of 0.0001 and batch size of 256. Refer to Algorithm 1 and 2 for details on training steps of CL and FL, respectively. Figure 11 in Appendix A.1 for more results.
As FL is performed without sharing any private email contents to the central repository (data always reside in the client), the convergences of the FL training and testing clearly show that the answer to the RQ1 is affirmative. Besides privacy, FL is computationally efficient than CL since the computation (ML training/testing) is distributed among the clients. In this setup, we reasonably assume that the email organisations (clients) are with resourceful computation to jointly training FL model to preserve the privacy of email samples.
RQ 2. How would the number of clients affect FL accuracy and convergence? Figure 4 illustrates the results from the measurement with two, five, and ten clients in FL. It shows that the maximum training accuracy of 99.794%, 99.576%, and 98.666% accuracy in FL with two, five, and ten clients, respectively. For the testing set, the maximum accuracy is 98.852%, 97.731%, and 96.85% for FL corresponding to two, five, and ten clients, respectively. It is not surprising that the convergence is slower, and performance gradually degraded with the increase in the number of clients. For more measurement results, refer to Figure 11 in Appendix A.1. It is expected that the number of clients/organisations participated in anti-phishing is usually limited, e.g., ten, correspondingly, the performance degradation resulted from an increasing number of participants is thus constrained.
Herein, we quantify the communication overhead in FL with two, five, ten, and twenty clients. For the overhead, we measure the data uploaded (i.e., a sum of the data packet size of W k t and n k ) and download (i.e., data packet size of W t +1 ) to and from the server, respectively, and it is averaged by the total number of the participating clients. In CL, we do not consider a client-server setup; thus, the communication overhead is zero. On the other hand, FL has communication overhead as a trade-off to preserve data privacy. For example, in two client setup, the average total communication size per global epoch per client is 0.178794768 GB and 0.178794749 GB during upload to the server and download from the server, respectively. Figure 5 illustrates the results. It shows that the communication overhead per client per global epoch is the same for all cases of up to twenty clients. The consistent communication overhead over multiple clients makes FL suitable for distributed training with a large number of (a) (b) Figure 4 : Convergence curves of (a) testing accuracy and (b) training accuracy in centralized and federated learning (FL) with two, five, and ten clients. clients. Moreover, the overhead can be easily addressed by a well-connected setup with wired or wireless connections between the server and clients participated in the anti-phishing framework. There is a slight difference in the upload size and the download size because at each client in FL, the download is always the weights of the model, whereas the upload is the weights of the model and the size of the local datasets (which is required to carry out weighted model averaging in server based on Algorithm 2).
So far, we have considered equal data distribution across the clients. Now in this measurement, we examine the performance of FL under an imbalanced data setup. In this regard, we consider two, five and ten clients, and variation in the local data sizes based on the maximum percentage of the variation provided by the term "var. " For example, if var = 10%, then the variation of the data across the five clients is given by [−10%, −5%, 0%, +5%, +10%], where -10% referred to the 10% less local data, and +10% referred to the 10% more data in the respective clients. This means, 3606, 3806, 4008, 4208, and 4408 local data samples are resided in clients 1, 2, 3, 4 and 5, respectively, if var = 10%. This way, we create a variation of the sizes of the local data by maintaining the total size of the datasets. In this measurement, we perform two experiments; firstly, by keeping the phishing to legitimate email samples ratio the same for all clients (i.e., 50:50 of phishing and legitimate email samples), and secondly with unequal ratio among clients. 4.4.1 Same phishing to legitimate email samples ratio across clients. The result of our measurement for 10%, 20%, 50%, and 80% variations in the sizes of the local dataset in FL among five clients is depicted in Figure 6 , which shows that the convergence of the test accuracy curves fluctuate slightly until the global epoch of 10, then remains stable afterwards. All cases with different var maintain an overall testing accuracy of around 98%. Similar trend is persistent in the training phase. Refer to Figures 18 and 19 in Appendix A.3 for more results. Besides, the convergence trend is also similar for the cases with two and ten clients. For details, refer to Fig Figure 7 depicts the results of our measurement for FL among five clients having the same size of the local dataset but all with (i) 10:90 (first case), (ii) 30:70 (second case), and (iii) 70:30 (third case) phishing to legitimate email samples (P/L) ratios. We choose the specific ratios for the test purpose so that the P/L ratio remains distinct. This setup is more practical than the setup with the same P/L ratio, as this has a bias in the samples. The measurements in this section have var = 0. The figure shows that until the global epoch of 15, there is a difference in the performance, where the first case (i.e., 10:90 P/L ratio) with the lower phishing email samples was not performing well compared with other cases with higher phishing email samples. However, after the epoch, all cases converge similarly to provide an overall testing accuracy of around 98% (refer to Figure 21 in Appendix A.3 for more results).
For the cases with two and ten clients, the results follow the similar pattern as observed in the case with five clients. However, the performance of the case with 10:90 P/L ratio jumps at different global epochs; jumps after 5 and 27 global epochs in the cases with two clients and ten clients, respectively. For details, refer to Figure 15 Based on the aforementioned results, the answer to the RQ4 is affirmative, and FL demonstrates a similar overall performance despite variations in the data distribution across clients. RQ 5 . Can we utilize the pre-trained model on the similar dataset for a better performance?
In this section, we perform three experiments to demonstrate the effects and benefits of transfer learning in phishing detection in distributed setup, which correspondingly answer the above research question (RQ5).
A client-level and overall effects of adding one new client in FL via transfer learning. In this experiment, we consider five clients in total, where the first four clients (C1-C4) participate in the FL until 15 global epochs and train the model collaboratively. Afterward, the transfer learning is carried out only with the fifth client, and the training proceeds for the next 15 global epochs (i.e., until 30 global epochs). In other words, the model is only trained by the fifth client for the last 15 epochs. Besides, for the performance evaluation, the testing results are computed for all five clients throughout the process. This experiment examines how a newly joining client member can perform FL to improve its performance in phishing detection compare to simply using the pre-trained model (on a similar dataset) for the detection.
The experimental result depicted in Figure 8 is for the case with var = 80, which provides the variations in the sizes of the local dataset (i.e., [-80%, -40%, 0%, +40%, +80%]) to capture a practical setting among the five clients. The figure shows that the average test accuracy of the first four clients is slightly higher than the fifth client (not participated in the learning process) until 15 global epochs. Afterward, the average testing accuracy of the fifth client improves by 2.6% than the others since its training dataset trains the model. This performance decreases with the lesser variation in the sizes of the local dataset; for example, the improvement is only 0.84% with var = 0. For more details, refer to Figure 27 , 29 and 30 in Appendix A.5. Overall results show that the evolved model (after training by client 5) is still relevant to the first four clients (C1-C4) as their average testing results with and without client 5 differ only nominally. Nonetheless, the fifth client boosts the accuracy of phishing detection in its local dataset by performing transfer learning under the FL setup.
A client-level and overall effects of continuously adding new clients in FL. In this experiment, the learning process is started with the first client, and then one new client is joined continuously at an interval of 10 global epochs as the training proceeds. Refer to Table 2 for details. This experiment simulates the practical cases where more than one client (different than the Experiment 1) is available with time during model training and demonstrates how the newly available clients can continue to perform FL to contribute accuracy improvements for phishing detection. Involvement of clients 0 to 9
Only the first client. 10 to 19 Only the first and second client. 20 to 29 Only the first, second and third client. 30 to 39 First, second, third and fourth client. 40 to 50
All five clients. The local test accuracy is measured for all clients throughout the process.
The result depicted in Figure 9 is for the case with the same size of the local dataset among the five clients, which (a) (b) Figure 9 : Convergence curves of (a) testing accuracy and (b) training accuracy from the Experiment 2 with five clients and var = 0. The FL training starts with one client, i.e., client 1, and a new client joins the training at every 10-th global epochs in a sequence from client 2 to client 5.
are gradually added to the learning process, as stated in Table 2 . As per expectation, it shows that the testing accuracy improves for each client when it is added to FL via transfer learning. For example, the average testing accuracy jumps by around 13% for client 2 when it joins client 1 in training the model at global epoch 10. The testing performance is carried out for all clients; however, the training result is carried only when the client is involved in the model training. Thus, the accuracy before a client joins the training is zero in Figure 9 (b). The performance pattern is similar for the case with var = 80 (refer to Figure 31 in Appendix A.6).
Benefits of the transfer learning to the newly participated client in the learning process. In this experiment, we analyze the performance of client 1, which we assume a newly participating client, with and without transfer learning. For this, we consider five clients with a variation in their dataset provided by var = 80 (this means that client 1 has 80% fewer data samples than client 3). In the transfer learning setup, the model is firstly trained by the four clients (client 2 to client 5) for 15 global epochs, and then the resulting model (pre-trained model) is further trained by client 1 on its local email data samples. On the other hand, for the case without transfer learning, client 1 performs CL on its local email dataset. The experimental result is depicted in Figure 10 . It shows that transfer learning outperforms CL along with a fast convergence for client 1. Moreover, at the global epoch of 45, the testing accuracy in transfer learning is 1.87% higher than CL. For more results, refer to Figure 32 in Appendix A.7.
Based on the results from the above three experiments in this section, it is clear that transfer learning is useful for the performance boosting in phishing detection under the federated setup.
A centralized email analysis based on AI-based methods for phishing detection has been explored for a long time.
Conventional ML-based techniques such as decision trees, logistic regression, random forests, AdaBoost, and support vector machines are analyzed in phishing detection [2, 4, 18, [45] [46] [47] . These techniques are based on feature engineering, which requires in-depth domain knowledge and trials.
On the other hand, DL-based methods include deep neural networks [43] , convolutional neural networks (CNNs) [26] , deep belief networks [50] , bidirectional LSTM with supervised attention [34] , and recurrent convolutional neural networks [11] . These works are mostly based on natural language processing techniques for phishing detection. While most existing works have focused on the effective detection of general phishing emails, there are few works that consider specialised phishing attacks, including spear phishing attacks [15] and business email compromise attacks [7] in specific contexts. Despite the usefulness, all the above works operate under a setting where emails must be centralized for analysis and thus do not provide privacy protection of email datasets.
There have been attempts on cryptographic approaches for supporting DL model training over encrypted data, which applies to the training of deep neural network models for phishing email detection while preserving privacy. In [31] , Mohassel and Zhang propose the first system design Se-cureML for privacy-preserving neural network training. In their system, multiple data providers can secretly share their data among two cloud servers, which will then conduct the training procedure over the secret-shared data. They rely on the secure computation techniques, e.g., secret sharing and garbled circuits, to design a secure two-party computation protocol, allowing two cloud servers to compute in the ciphertext domain the linear operations (addition and multiplication) as well as the non-linear activation functions. Later, Wagh et al. [48] propose a design that works in the threeserver model and is purely based on the lightweight secret sharing technique, with better performance than SecureML. This work assumes an adversary model where none of the three cloud servers will deviate from the protocol. The work in [30] also operates under a similar three-server setting, yet achieves more robust security against malicious adversaries who deviate arbitrarily. This line of work presents valuable research endeavours in enabling deep neural network training over encrypted data. Yet, it has to rely on additional architectural assumptions (i.e., non-colluding cloud servers) and also incurs substantial performance overheads (up to orders of magnitude slower) when compared to the plain text baseline.
FL is attractive, especially when the data is sensitive, like in the financial sector (banks) and the medical sector (hospitals) [41] . There have been several works in FL though none of them specifically address phishing email detection. Some works include the following: Google has used FL for next-word prediction in a virtual keyboard for smartphones words [19] , Leroy et al. applied FL for speech keyword spotting [25] , Gao et al. [13] propose to use FL to train a joint model over heterogeneous ECG medical data to preserve the data privacy of each party, and Yang et al. [49] applied FL to detect credit card fraud.
There are various other techniques such as homomorphic encryption [16] (a cryptographic approach) and differential privacy [9] used along with FL for guaranteed and provable privacy, respectively. However, homomorphic encryption increases computational overhead, and differential privacy degrades the performance as a trade-off. The integration of these techniques to FL in phishing detection remains as future work.
This work took the first step to implement federated learning (FL) for privacy-preserving email phishing detection. Built upon the state-of-art deep learning model that is delicately designed for email phishing detection as a centralized learning baseline, our comprehensive measurements under FL demonstrated promising results while preserving the privacy of the email content. More specifically, the deep learning model performance under FL was as good as that of centralized learning under various practical scenarios, including imbalanced data distribution among clients. Besides, this work leveraged transfer learning to enable fast convergence of accuracy curves and improved accuracy in client-level phishing detection. In FL, the email data samples always reside in the email data custodians, e.g., participating organizations in the phishing detection from different geographic locations, and data is not shared among the participants. Thus, considering this inherent privacy-preservation feature, it potentially unleashes the willingness of more clients (thus more data) contributing to the deep learning in email phishing detection to improve the model performance, including the client-level performance, by harnessing data integration in FL as illustrated in this paper.
The work is partially supported by the Cyber Security Cooperative Research Centre, Australia. Authors acknowledge Professor Rakesh Verma from the University of Houston, USA, for the IWSPA-AP corpus. A.4 Imbalanced data setup -ten clients (additional cases) 