Fedchain a collaborative framework for building artificial intelligence models using blockchain and federated learning

FedChain A Collaborative Framework for Building Artificial Intelligence Models Using Blockchain and Federated Learning FedChain A Collaborative Framework for Building Artificial Intelligence Models us[.]

2021 8th NAFOSTED Conference on Information and Computer Science (NICS) FedChain: A Collaborative Framework for Building Artificial Intelligence Models using Blockchain and Federated Learning Tran Duc Luong∗† , Vuong Minh Tien∗† , Hoang Tuan Anh∗† , Ngan Van Luyen∗† , Nguyen Chi Vy∗† , Phan The Duy∗† , Van-Hau Pham∗† ∗ Information Security Laboratory, University of Information Technology, Ho Chi Minh city, Vietnam † Vietnam National University, Ho Chi Minh city, Vietnam Ho Chi Minh City, Vietnam {19521815, 19522346, 18520446, 18521074, 18521681}@gm.uit.edu.vn, {duypt, haupv}@uit.edu.vn Abstract—Machine learning (ML) has been drawn to attention from both academia and industry thanks to outstanding advances and its potential in many fields Nevertheless, data collection for training models is a difficult task since there are many concerns on privacy and data breach reported recently Data owners or holders are usually hesitant to share their private data Also, the benefits from analyzing user data are not distributed to users In addition, due to the lack of incentive mechanism for sharing data, ML builders cannot leverage the massive data from many sources Thus, this paper introduces a collaborative approach for building artificial intelligence (AI) models, named FedChain to encourage many data owners to cooperate in the training phase without sharing their raw data It helps data holders ensure privacy preservation for the collaborative training right on their premises, while reducing the computation load in case of centralized training More specifically, we utilize federated learning (FL)and Hyperledger Sawtooth Blockchain to set up a prototype framework that enables many parties to join, contribute and receive rewards transparently from their training task results Finally, we conduct experiments of our FedChain on cyber threat intelligence context, where AI model is trained within many organizations on each their private datastore, and then it is used for detecting malicious actions in the network Experimental results with the CICIDS-2017 dataset prove that the FL-based strategy can help create effective privacy-preserving ML models while taking advantage of diverse data sources from the community Index Terms—Federated Learning, Privacy Preservation, Blockchain, Generative Adversarial Networks I I NTRODUCTION Currently, more and more countries have been promoting the strategy of building Smart City to catch up with the growth of The Fourth Industrial Revolution where AI takes a leading role In reality, AI models leverage the advances in ML to build more efficient models, which requires a large amount of data for training The centralized approach to building ML models today is to gather all the training data in a particular server, usually in the cloud, and then train the model on the collected data volume However, this approach is slowly becoming unfeasible in practice for privacy and security reasons when collecting data The risk of sensitive data loss during storage, transmission, and sharing of data has raised 978-1-6654-1001-4/21/$31.00 ©2021 IEEE concerns about the privacy of data owners At the same time, data providers are also unwilling to share data because their data contributions would not earn them any awards in the traditional ML training method Therefore, for the sake of comprehensive smart ecosystems, a new training mechanism must be discovered and implemented which could resolve the issues of data security and the incentives in training a mutual model In this context, Federated Learning (FL) emerges as a decentralized learning technique that ensures both the high performance of ML models and data privacy Contrary to centralized learning, this method allows the global model to be trained right on the local parties and transfers the model parameters to the central server, which is responsible for the aggregation of the received model updates to construct an improved global model Finally, the participants download the global update from the aggregator and compute it on their own dataset for the next local model The training process occurs iteratively until the global training is optimized By utilizing the computing strength of distributed clients, FL approach can enhance ML model quality and reduce user privacy leakage Nevertheless, FL scheme in practice has to face nonindependent and identically distributed (Non-IID) data that means unbalanced data distribution in size, labels, etc among collaborative workers Many researchers have indicated that the degradation in accuracy and performance of FL appears almost inevitable due to Non-IID data With the power of generating synthetic samples, Generative Adversarial Networks (GAN) would be used as a data augmentation technique to mitigate this imbalance issues in FL The operating principle of GAN can be visualized as a zero-sum game between two opposing neural networks, the Generator (G) and the Discriminator (D) G will be trained to output new adversarial samples given some noise source, whereas D is responsible for classifying data samples as either real (from the original dataset) or fake (generated from G) The game would go on continuously until the D model can no longer distinguish real or fake samples, meaning the G model is generating plausible data Then in FL scheme, each client would be equipped with 149 2021 8th NAFOSTED Conference on Information and Computer Science (NICS) a GAN architecture which is used for data augmentation in the case of Non-IID data In building ML models, most works assume that all devices in the system will engage in FL tasks unconditionally when requested, and will be totally honest It is, however, impossible because there will be incurred expenses and many dishonest participants during the model-building process Therefore, to create effective models, it is important that the system must ensure the honesty of the participants and have some incentive mechanism to offer the contributed resources with the appropriate rewards With Blockchain, all contributions will be stored transparently, which means that each node in the Blockchain can check the validity of the contribution From there, a framework may be built to help manage, control honesty and encourage data owners to be involved in building models through a reward mechanism in accordance with contributions Besides, the application of the InterPlanetary File System (IPFS) can help the model to achieve complete disintegration With distributed data storage, IPFS helps the system to strengthen fault tolerance and eliminate the drawbacks of centralized data storage systems This is also conducive to the system to significantly optimize costs in terms of time and data transmission Based on the above-mentioned analysis, in this paper, we propose a new privacy-preserving framework named FedChain using FL and GAN in the process of training AI models efficiently, even in Non-IID data Also, Blockchain and IPFS will be integrated to provide transparency, honesty along with an incentive mechanism for collaborative learning Finally, to evaluate the feasibility and effectiveness of FedChain framework, Intrusion Detection System (IDS) is selected as the ML model due to the urgency of cybersecurity factors in the smart city development The rest of this paper is organized as follows In Section II, we introduce the related works on FL, GAN, and Blockchain In Section III, we present an overview and describe the detailed operating procedure of FedChain system The environmental setup and performance evaluation results are shown in Section IV Finally, we conclude the paper and propose future works in Section V II R ELATED WORKS A Federated Learning and GAN Federated Learning is a distributed collaborative AI mechanism that appealed to the attention of many researchers in various fields With the capability of training ML model in a distributed manner, FL addresses critical issues about privacy and security of data left by the centralized approach Some previous science papers [1], [2], [10], [12], [13] have studied the applications of FL in the context of IIoT Specifically, Dinh C.Nguyen et al [1] carried out a comprehensive FL survey that discussed the role of FL in a wide range of IoT services such as IoT data sharing, data offloading, and caching, attack detection, mobile crowd-sensing, and IoT privacy and security The authors also proved the flexibility of FL in some areas such as smart healthcare, smart transportation, Unmanned Aerial Vehicles (UAVs), etc On the side of network security, Shaashwat Agrawa et al [2] introduced an FLbased framework for Intrusion Detection System (IDS) with the aim of enhancing anomaly detection accuracy as well as user privacy The paper then presented other challenges and potential solutions for FL implementations in IDS Recently, many researchers have been devoted to implementing Generative Adversarial Networks (GAN) as a data generation scheme in FL scenarios However, instead of drawing on any systematic approach to the beneficial aspect of GAN, a variety of writers utilized this generative data method to conduct causative attacks [3], [4] in the context of FL Jiale Zhang et al [3] proposed a poisoning attack strategy that unfriendly participants aim to deteriorate the performance of the global model by fabricating malicious adversarial data with a GAN architecture B Federated Learning with Blockchain in IIoT On the issue of building Blockchain-Based FL for IoT Devices framework, there have been many research efforts focused on this issue A few previous research papers [5], [6], have studied the building of a secure FL framework based on empowerment for Blockchain technology The prominent investigation of Rui Wang et al [5] is the first to integrate the Blockchain framework and MEC technology into the FL scenario to ensure the privacy, quality, and communication overhead for the IoV system The authors also proposed an algorithm to prevent malicious updates to protect FL and design an incentive mechanism based on trained model weights In previous research on strategies to enhance training and privacy in FL, Swaraj Kumar et al [7] used InterPlanetary File System (IPFS) as a data storage to build a fully decentralized system They also created a value-driven incentive mechanism using Ethereum smart contracts Besides that, Yufeng Zhan et al [8] presented a classification of existing incentive mechanisms for associative learning and then evaluated and compared them The authors pointed out incentive mechanisms that have been ignored or are currently not linked to the algorithms in the current models The authors suggested that building an incentive mechanism also needs to construct a more secure system so that users can feel safe to participate in model training Nonetheless, they just raised ideas on building incentive mechanisms in FL without implementation III S YSTEM DESIGN : F ED C HAIN A Overview of Architecture The overall architecture of FedChain is illustrated in the Fig The system consists of network layers that leverage Blockchain to link clients and servers in FL At the lowest layer, participants download the global model from the server and train the model locally by their collected data Concerning Non-IID data, GANs will be used to generate adversarial data as a supplier for the client dataset The next layer includes Blockchain management and the IPFS database Following identity verification, the client can register nodes and task 150 2021 8th NAFOSTED Conference on Information and Computer Science (NICS) to update the model The process goes back to step and perform until the specified number of rounds is reached, or the model is converged Fig The architecture of FedChain system training using Blockchain The system allows combining multiple tasks to provide a variety of task training All data at this tier will be stored distributedly via IPFS to make our system fully decentralized The highest layer is the aggregation server, whose responsibility is to aggregate the models received in the lower layer after executing smart contracts All operations in the system are recorded to the Blockchain as transactions for aggregation and evaluation Blockchain helps to ensure transparency, security, immutability, and auditability in the payment of rewards and distribution of profits after completing the FL tasks Our system is designed to ensure a secure collaborative framework for training ML models by combining FL with blockchain and IPFS The system can ensure privacy, transparency, low communication overhead, as well as encourage data owners to participate in the FL process to build mutual ML models B Training ML Model with Federated Learning The workflow of training ML model with FL approach is described in four steps as described in Algorithm Firstly, the aggregation server initializes the global model with parameters w0 The number of rounds, epochs, clients, etc of the model is defined These parameters are sent to training devices from many organizations, which take part in the training process Next, these devices train the local model by self-collected dataset and send new parameters wkr back to the aggregation server This is conducive to preserving the data privacy of local organizations instead of sending raw data Thirdly, the server aggregates the received models based on the FedAVG algorithm [9] The global model parameters wr are calculated depending on the data contribution rate of each individual used for training Finally, the aggregation server distributes the global model to the participants so that they can continue Algorithm Federated Learning-based ML model training process Input: - Aggregation Server AS; - Clients C = {(Ck ); k = 1, 2, , n}, that want to join the training process; - The number of exchange rounds R between server and participant Output: The efficient machine learning model 1: Initialization: 2: - At Aggregation Server: A Machine Learning Model M is generated parameter w0 with initial settings 3: - At Local Server: Clients C receive model M with parameter w0 and related parameters 4: Procedures: 5: r ← 6: snod ← specified number of datasets 7: while r ≤ R 8: Training local machine learning model 9: k←1 10: while k ≤ n 11: cd ← Client Ck datasets 12: if cd < snod then 13: while cd < snod 14: GAN generates and provides datasets 15: end while 16: end if 17: Client Ck train model Mk using private datasets 18: Send back parameter wkr to AS 19: k ←k+1 20: end while 21: Aggregation Server builds new model 22: - AS receive all parameter wkr from all clients 23: - AS uses FedAVG algorithm and generate new model with parameter wr 24: Aggregation Server sent new model to participant 25: k←1 26: while k ≤ n 27: Client Ck update parameter wr to model Mk 28: k ←k+1 29: end while 30: r ←r+1 31: end while 32: return Machine Learning Model with parameter w R C Incentive Mechanism for Collaborative Training using Blockchain The architecture we provide is autonomous, serving the connection between those who have data and those who want to create ML models from that data 151 2021 8th NAFOSTED Conference on Information and Computer Science (NICS) Blockchain is characterized by transparency; once data is written to the blockchain, it cannot be deleted or changed by any individual or organization Based on that feature, we decided to use Blockchain to record user behavior and use it as evidence for the rewarding process In this way, the payout is fair and transparent The main user behaviors handled within the blockchainbased system are described as follows: • • • • • System membership registration: During the development of the functions, we use asymmetric encryption to authenticate the user’s identity Therefore, for the member registration task, in addition to the necessary information, the user needs to generate a key pair The public key will be sent with the request Register to open a new training task: When an individual or organization wants to open a new training assignment, they need to submit identification and information about the task they want to open When a task is successfully registered, everyone in the system can see it Those with the right dataset will register and contribute to this task Register to contribute to a specific task: Users will choose a task published on the system that matches the dataset they own to participate in the contribution process for that task To perform this function, users need to submit information about hardware resources and characteristics of the dataset they own Registration information is stored for tracking and a global model is sent back to the user for training Upload local model: After receiving the training task, users go through the training process on their dataset Then, the results of the training process are sent to the system with the task name To reduce the load on the Blockchain system, we use IPFS, a distributed database, to store uploaded files In this way, it is possible to reduce communication costs and overcome the disadvantages of traditional centralized data storage When the model is uploaded, it will be evaluated and the results are stored in Blockchain These contributions are considered to compensate users accordingly Collect model: This function is performed by the task publisher They will ask the system to retrieve the models related to the task that they have previously registered The system, after receiving the request, will verify the identity and return the models corresponding to the task All the above activities occurring in the system are recorded in the Blockchain, ensuring transparency in the operation process, which increases incentives for participants to contribute D Data Augmentation for Clients using GAN The GAN architecture in FedChain framework is equipped in each client that is responsible for automatically supplying new data records in the case of Non-IID data After the training phase as described in Algorithm 2, the well-trained generator G can generate adversarial examples from a given noise vector Algorithm GAN training process The generator G and discriminator D are trained in a parallel manner Input: - Original dataset x including benign samples and malicious samples; - The noise vector z for generator; Output: The optimization of generator G and discriminator D; Procedure: 1: for number of training iterations do: 2: D training: 3: - Sample minibatch of m noise samples {z1 , z2 , , zm } from noise distribution pz (z) 4: - Sample minibatch of m real samples {x1 , x2 , , xm } from data generating distribution pdata (x) 5: - Update D loss by ascending its stochastic gradient: m X [log D (xk ) + log (1 − D(G(zk )))] m k=1 6: 7: 8: G training: - Sample minibatch of m noise samples {z1 , z2 , , zm } from noise distribution pz (z) - Update G loss by descending its stochastic gradient: m X [log (1 − D(G(zk )))] m k=1 9: 10: end for return GAN Model IV E XPERIMENT In fact, the FedChain framework can support the construction of AI models in various fields Within the scope of the paper, this framework is utilized to build ML models in the context of network intrusion detectors, which is conducive to the aspect of cybersecurity This part presents the experimental results of the trained ML-IDS model through FedChain to prove the feasibility of our approach A Environmental Settings Environment for FL and GAN Tensorflow is used for FL implementation, while GAN is carried out on Keras Tensorflow The hardware configuration used for training FL and GAN is CPU: Intel® Xeon® E5-2660 v4 (16 cores – 1.0 Ghz), RAM: 16 GB, OS: Ubuntu 16.04 Environment for Blockchain system We use Hyperledger Sawtooth platform with PBFT consensus algorithm deployed on nodes to build Blockchain system with Docker The machine configuration used for this deployment is CPU Intel® Core™ i5-9300HQ (4 cores - threads - 3.5 Ghz), RAM 16 GB, OS Ubuntu 16.04 B Dataset and Preprocessing Dataset This study would utilize a recent dataset named CICIDS2017 provided by the Canadian Institute of Cybersecurity 152 2021 8th NAFOSTED Conference on Information and Computer Science (NICS) for the evaluation of FedChain scheme It contains over 2.8 million network flows spanned in csv files including benign samples and 14 types of up-to-date attacks However, we only spend the data from files (Tuesday, Wednesday, ThursdayAfternoon) with a total of more than 1.3 million network records that describe typical cyberattacks such as DoS Hulk, DoS Golden Eye, DoS slowloris, DoS Slowhttptest, Heartbleed, FTP-Patator, SSH-Patator, Infiltration More specifically, from the collected records in each file, we divide them into sub-datasets with the ratio of 80:20 that the larger part is the training set, and the other is used for testing phases Finally, training and testing sets from files would be combined into CICIDS2017 Train and CICIDS2017 Test files, respectively Data Preprocessing According to a study published in the article [11], Kurniabudi et al showed that the top 16 features in the CICIDS2017 dataset are believed to be easily extracted and observed, as well as having a great influence on detecting a basic network attack These selected features are Destination Port, Flow Duration, Packet Length Std, Total Length of Bwd Packet, Subflow Bwd Bytes, Packet Length Variance, Bwd Packet Length Mean, Bwd Segment Size Avg, Bwd Packet Length Max, Total Length of Fwd Packets, Packet Length Mean, Max Packet Length, Subflow Fwd Bytes, Average Packet Size, Init Win bytes backward, Init Win bytes forward After feature selection, we remove all redundant features, delete non-numeric fields (NaN) and infinity values (Inf) Next, the values of label column will be converted to binary format where labels represents benign samples and labels are assigned to the others In our experiments, we have rescaled 16 features to the interval [0,1] via Min-Max normalization which has the following formula: xrescaled = Fig Similarity between original and generated data in features (From left to right): FlowDuration, TotalLengthBwdPacket, PacketLengthMean, PacketLengthStd Comparison of FL model performance on Non-IID data without and with GAN The ML-IDS model is implemented in FL scenario based on LSTM model with the following layers: LSTM layer with 64 internal units, Dense layer with 16 internal units The model has an input of size (16,1) and output is the result after passing the Dense layer with the sigmoid activation function We conduct FL training in 3,6,9 and 12 rounds (R) with clients (K) TABLE I T HE RESULT OF THE EXPERIMENT IN THE N ON -IID DATA CASE Without GAN x − xmin xmax − xmin where x is the feature value before the normalization and xrescaled is that after the normalization Besides, xmax and xmin represent the maximum value and the minimum value of this feature in the dataset, respectively C Performance evaluation GAN performance in generating adversarial data This experiment will show the ability of GAN in automatically generating new network traffics that closely resemble the input ones We construct GAN architecture with the following hyperparameters: epochs = 5000, batch size = 256, using Adam optimizer with learning rate = 0.0002 Also, the generator G and discriminator D are designed with and layers in turn Both of them utilize LeakyReLU (Leaky Rectified Linear Unit) and sigmoid as activation functions Following the GAN training procedure, we proceed to utilize generator G to generate new synthetic samples from CICIDS2017 Train dataset A glance at the Fig.2 reveals the resemblance between original CICIDS2017 Train and generated network flows in four above features With GAN Round 12 12 Precision 0.9035 0.9482 0.9725 0.9413 0.9457 0.9541 0.9129 0.959 Recall 0.6342 0.591 0.5778 0.5696 0.9021 0.9026 0.9645 0.9007 F1-score 0.7452 0.7281 0.7249 0.7097 0.9233 0.9276 0.9379 0.9289 Accuracy 0.9564 0.9557 0.9559 0.9532 0.9331 0.9371 0.9612 0.9581 Table I compares the performance of the model before and after using GAN in terms of the four above metrics In the beginning, the input data ratio among clients is 5:2:3, whereas the size of the input dataset among them after using GAN becomes balanced As a result, the training model with GAN has witnessed a surge in recall and F1-score by approximately 39% (R=9) and 22% (R=12) respectively The other metrics still stabilize at a high level, about 95% Blockchain performance in FL context We evaluate the performance of the Blockchain system through CPU resource consumption and request processing time in some specific contexts To determine the CPU resource consumption of the system when operating, we conducted a test with 10 users continuously submitting a request to register a task and measured the consumption at each interval of 0.1 seconds The results are presented in the Fig.3 153 2021 8th NAFOSTED Conference on Information and Computer Science (NICS) investment and operating costs by combining practical and effective technologies such as FL, GAN, Blockchain, IPFS with high applicability and flexibility The experimental results of our analysis on cyber threat intelligence context with the CICIDS-2017 dataset have demonstrated that the proposed FedChain can enable data sharing securely and efficiently that takes advantage of diverse data sources from the community In the future, we plan to integrate mobile edge computing (MEC) into the FedChain framework to reduce the system’s communication and data transmission costs CPU Usage (percent) 30 25 20 15 ACKNOWLEDGEMENT Phan The Duy was funded by Vingroup Joint Stock Company and supported by the Domestic Master/ PhD Scholarship Programme of Vingroup Innovation Foundation (VINIF), Vingroup Big Data Institute (VINBIGDATA), code VINIF.2020.TS.138 10 0.5 Time (second) 1.5 Fig CPU consumption results R EFERENCES To measure the system’s processing time performance, we measured the processing time in turn in the context of 10 users, 20 users, 50 users continuously sending task registration requests to the system Measurements were repeated three times to ensure accuracy The average processing time results are presented in Table II TABLE II R ESULTS OF MEASURING THE SYSTEM ’ S PROCESSING TIME ( S ) 10 User 20 User 50 User Round 0.06023118 0.103943 0.336669 Round 0.059109 0.101376 0.384335 Round 0.059569 0.112528 0.297349 Next, we conduct performance testing in the context of processing the upload model task The obtained results show that the system takes an average of 25.19693 seconds to process a request To handle this request, the system needs to process it in steps The first step is to upload the model to IPFS; the second step is to write the data to the Blockchain We have calculated and found that most of the time is spent on uploading the model to IPFS (25.19693 seconds/request for the above context) while the Blockchain system works very well (0.018419 seconds/request) However, uploading the model to IPFS completely depends on the connection speed of the system, from which it can be concluded that our system can still work well From the above results, we can see that the performance of Blockchain is great regarding both processing time and CPU resource consumption V C ONCLUSION AND F UTURE W ORK In the evolution of the artificial intelligence industry, data sharing plays an essential role in building intelligent ecosystems and applications In this paper, we have proposed a collaborative framework named FedChain to help ensure data privacy and security, along with an incentive and transparent mechanism In addition, FedChain helps to optimize resources, [1] Dinh C Nguyen, Ming Ding, Pubudu N Pathirana, Aruna Seneviratne, Jun Li, and H Vincent Poor, ”Federated Learning for Internet of Things: A Comprehensive Survey,” IEEE Communications Surveys & Tutorials, 2021 [2] Shaashwat Agrawal, Sagnik Sarkar, Ons Aouedi, Gokul Yenduri, Kandaraj Piamrat, Sweta 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