Publications

This book provides a comprehensive analysis of fundamental topics related to blockchain. Throughout, the authors explore different vital issues and specific areas of blockchain. For convenience, the authors present the elementary description, visualize the working procedure of blockchain paradigm, and highlight the areas it can be applied in real life. They explain the blockchain process from a diverse perspective i.e. distributed Internet of Things (IoT), interdependent networks, intelligent mining, etc. They also analyze the interconnection of a blockchain network and such novel research areas to show a pathway towards a new research direction. This book also holds the core challenges and open research issues of blockchain technology, considering existing applications. Chapters include consensus mechanisms of blockchain, blockchain applicability in centralized and decentralized internet of things, blockchain interoperability from the perspective of interdependent networks, and blockchain for resource-constrained devices.

Will be available soon!

Will be available soon!

Will be available soon!

Will be available soon!

Will be available soon!

Will be available soon!

The advancement of technology, improvement of network infrastructures, and wide availability of internet open up the door of new opportunities to perform on-device inference. Realizing the potential of such advancement, Federated Learning (FL) was invented that facilitates the formation of a powerful model without exposing user data. While successful, it does not consider the combinational case, where the selected FL agents independently craft their local model with heterogeneous architecture and perform computational tasks based on their available resources. In the original FL model, all agents need to agree on a uniform model architecture and are assigned a uniform computational task. However, in a real-life resource-constrained FL setting, agents may not be interested to share their local model architecture details due to privacy and security concerns. Also, the heterogeneous local model architectures cannot be aggregated together on the FL server following the traditional approaches. Moving forward, we may observe straggler agents due to resource-constrained environments, such that any FL agent may find a task as computationally challenging that can prolong the model convergence. To address the above-mentioned challenges regarding agent’s local model and resource heterogeneity, we propose an FL framework, FedMDP that can effectively handle federated agents possessing nonidentical local model structure as well as variant local resources using knowledge distillation and dynamic local task allocation techniques. We tested our framework on MNIST and CIFAR100 dataset and observed significant improvement in accuracy in a highly heterogeneous environment. By considering 10 uniquely designed model of the agents, we achieved 15% gain on average compared to the accuracy of the traditional learning methods and observed a few percent lower accuracy compared to the case if the agents’ local datasets were pooled and made available for all the network agents.

Federated Learning (FL) has gained widespread popularity in recent years due to the fast booming of advanced machine learning and artificial intelligence along with emerging security and privacy threats. FL enables efficient model generation from local data storage of the edge devices without revealing the sensitive data to any entities. While this paradigm partly mitigates the privacy issues of users sensitive data, the performance of the FL process can be threatened and reached a bottleneck due to the growing cyber threats and privacy violation techniques. To expedite the proliferation of FL process, the integration of blockchain for FL environments has drawn prolific attention from the people of academia and industry. Blockchain has the potential to prevent security and privacy threats with its decentralization, immutability, consensus, and transparency characteristic. However, if the blockchain mechanism requires costly computational resources, then the resource-constrained FL clients cannot be involved in the training. Considering that, this survey focuses on reviewing the challenges, solutions, and future directions for the successful deployment of blockchain in resource-constrained FL environments. We comprehensively review variant blockchain mechanisms that are suitable for FL process and discuss their trade-offs for a limited resource budget. Further, we extensively analyze the cyber threats that could be observed in a resource-constrained FL environment, and how blockchain can play a key role to block those cyber attacks. To this end, we highlight some potential solutions towards the coupling of blockchain and federated learning that can offer high levels of reliability, data privacy, and distributed computing performance

Differentially-Private Federated Learning (DPFL) is an emerging privacy-preserving distributed machine learning paradigm that allows for the automatic recognition of human activities using wearable sensors without compromising users’ sensitive data. However, this decentralized approach makes the system vulnerable to poisoning attacks, where malicious agents can inject contaminated data during local model training. This paper presents the results of our research on designing, developing, and evaluating a holistic model for data poisoning attacks in DPFL-based human activity recognition (HAR) systems. Specifically, we focus on label-flipping poisoning attacks, where the label of a sensor reading is maliciously changed during data collection. To investigate the impact of such attacks, we develop a simulator that explores key design issues, such as the correlation between the level of differential privacy, the level of poisoning, the number of communication rounds, and the number of agents in the system. Our findings shed light on the effectiveness of label contamination attacks in DPFL-based HAR systems and can inform the development of more robust and secure models.

Connected autonomous vehicles (CAVs) are transforming the transportation business by incorporating advanced technology such as sensors, communication systems, and artificial intelligence. However, the interconnectedness and complexity of CAVs pose security vulnerabilities, making them possible targets for assaults. Intrusion detection is critical in protecting CAVs from harmful actions. This research investigates the use of federated learning, a privacy-preserving machine learning approach, for intrusion detection in CAVs. Federated Learning (FL) can improve the detection capabilities and robustness of intrusion detection systems in the CAV ecosystem by using the collective capacity of various CAVs while protecting data privacy. This paper provides an in-depth analysis of tailoring FL for collaborative intrusion detection in CAVs, as well as prospective future research areas in this domain. The findings of this study contribute to the advancement of secure and dependable CAV systems, opening the path for the widespread use of connected autonomous vehicles in the transportation industry. All code, data, and experiments are accessible on our Github1 repository

Federated Learning (FL) has recently experienced tremendous popularity due to its emphasis on user data privacy. However, the distributed computations of FL can result in constrained communication and drawn-out learning processes, necessitating the client-server communication cost optimization. The ratio of chosen clients and the quantity of local training passes are two hyperparameters that have a significant impact on the performance of FL. Due to different training preferences across various applications, it can be difficult for FL practitioners to manually select such hyperparameters. In this paper, we introduce FedAVO, a novel FL algorithm that enhances communication effectiveness by selecting the best hyperparameters leveraging the African Vulture Optimizer (AVO). Our research demonstrates that the communication costs associated with FL operations can be substantially reduced by adopting AVO for FL hyperparameter adjustment. Through extensive evaluations of FedAVO on benchmark datasets, we identify the optimal hyperparameters that are appropriately fitted for the benchmark datasets, eventually increasing global model accuracy by 6% in comparison to the state-of-the-art FL algorithms (such as FedAvg, FedProx, FedPSO, etc.).

Federated learning (FL) is a distributed machine learning strategy that generates a global model by learning from multiple decentralized edge clients. FL enables on-device training, keeping the client’s local data private, and further, updating the global model based on the local model updates. While FL methods offer several advantages, including scalability and data privacy, they assume there are available computational resources at each edge-device/client. However, the Internet-ofThings (IoT)-enabled devices, e.g., robots, drone swarms, and low-cost computing devices (e.g., Raspberry Pi), may have limited processing ability, low bandwidth and power, or limited storage capacity. In this survey article, we propose to answer this question: how to train distributed machine learning models for resource-constrained IoT devices? To this end, we first explore the existing studies on FL, relative assumptions for distributed implementation using IoT devices, and explore their drawbacks. We then discuss the implementation challenges and issues when applying FL to an IoT environment. We highlight an overview of FL and provide a comprehensive survey of the problem statements and emerging challenges, particularly during applying FL within heterogeneous IoT environments. Finally, we point out the future research directions for scientists and researchers who are interested in working at the intersection of FL and resource-constrained IoT environments.

Nowadays, there is an ever-increasing deployment of intelligent edge devices, such as smartphones, wearable devices, and autonomous vehicles. It is enabled by the integration of advanced sensors with higher computing capabilities and widespread internet availability. These edge devices generate a vast amount of data that can be utilized for better inference. However, due to privacy concerns, communication overhead, processing delay, and security issues, traditional machine learning (ML) algorithms face challenges that work in a centralized fashion where all the available data is accumulated beforehand. Federated learning (FL) is a new distributed on-device learning method that generates a global model through the collaboration of edge devices without compromising data privacy. In this paper, we propose a federated transfer learning (FTL) model considering clients’ heterogeneity in terms of their available computing resources and model architecture. We simulate the training performance of heterogeneous clients and observe that clients with sufficient resources require significantly lower computational time. In turn, the resource-constrained clients take notably higher computational time to accomplish a given task. Inspired by that, we design an FL model that constructs multiple global models based on the available resources of the clients and carries out a separate training process for each of the global models. We demonstrate the effectiveness of our proposed strategy by evaluating our FL model on CIFAR100 dataset. Our findings show that training time differs significantly among the heterogeneous clients and assigning

With the rapid emergence of technologies, coupling interdependent consumer electronics networks have become imperative to enable information exchange, holistic data analysis, and effective decision-making techniques. While interdependent networks can discover crucial information through intuitive understanding and efficient data-driven algorithms, it is crucial to leverage secure and effective data exchange among the underlying networks of consumer electronics. Blockchain comes into play when it requires ensuring trustless communication and data exchange amongst multiple interdependent networks. Blockchain interoperability is evolving and gaining widespread popularity in industry and research due to its ability to exchange data among multiple blockchains. This paper presents Divide-LeapChain, a next-generation efficient and effective interoperable blockchain framework for interdependent networks of heterogeneous consumer electronics devices. The Divide-LeapChain addresses the critical issues of intra- and inter-blockchain communication, including spatial and temporal uncertainties in data traversal, exchange, validation, and cross-chain swaps. It promises significant drops-off in verification steps without weakening blockchain integrity, which extends the possibilities to apply blockchain applications on heterogeneous embedded consumer Internet-of-Things (IoT) devices

Smartphones, autonomous vehicles, and the Internet-of-things (IoT) devices are considered the primary data source for a distributed network. Due to a revolutionary breakthrough in internet availability and continuous improvement of the IoT devices capabilities, it is desirable to store data locally and perform computation at the edge, as opposed to share all local information with a centralized computation agent. A recently proposed Machine Learning (ML) algorithm called Federated Learning (FL) paves the path towards preserving data privacy, performing distributed learning, and reducing communication overhead in large-scale machine learning (ML) problems. This paper proposes an FL model by monitoring client activities and leveraging available local computing resources, particularly for resource-constrained IoT devices (e.g., mobile robots), to accelerate the learning process. We assign a trust score to each FL client, which is updated based on the client’s activities. We consider a distributed mobile robot as an FL client with resource limitations either in memory, bandwidth, processor, or battery life. We consider such mobile robots as FL clients to understand their resource-constrained behavior in a real-world setting. We consider an FL client to be untrustworthy if the client infuses incorrect models or repeatedly gives slow responses during the FL process. After disregarding the ineffective and unreliable client, we perform local training on the selected FL clients. To further reduce the straggler issue, we enable an asynchronous FL mechanism by performing aggregation on the FL server without waiting for a long period to receive a particular client’s response.

In this paper, we first introduce some recently implemented real-life applications of FL. We then emphasize on the core challenges of implementing the FL algorithms from the perspective of resource limitations (e.g., memory, bandwidth, and energy budget) of client devices. We finally discuss open issues associated with FL and highlight future directions in the FL area concerning resource-constrained devices.

The resilient operation of power distribution networks requires efficient optimization models to enable situational awareness. One of the pivotal tools to enhance resilience is a network reconfiguration to ensure secure and reliable energy delivery while minimizing the number of disconnected loads in outage conditions. Power outages are caused by natural hazards, e.g., hurricanes, or system malfunction, e.g., line failure due to aging. In this paper, we first propose a distribution-network optimal power flow formulation (DOPF) and define a new resilience evaluation indicator, the demand satisfaction rate (DSR). DSR is the rate of satisfied load demand in the reconfigured network over the load demand satisfied in the DOPF. Then, we propose a novel model to efficiently find the optimal network reconfiguration by deploying sectionalizing switches during line outages that maximize resilience indicators. Moreover, we analyze a multiobjective scenario to maximize the DSR and minimize the number of utilized sectionalizing switches, which provides an efficient reconfiguration model preventing additional costs associated with closing unutilized sectionalizing switches. We tested our model on a virtually generated 33-bus distribution network and a real 234-bus power distribution network, demonstrating how using the sectionalizing switches can increase power accessibility in outage conditions.

In recent years, as economic stability is shaking, and the unemployment rate is growing high due to the COVID-19 effect, assigning credit scoring by predicting consumers’ financial conditions has become more crucial. The conventional machine learning (ML) and deep learning approaches need to share customer’s sensitive information with an external credit bureau to generate a prediction model that opens up the door of privacy leakage. A recently invented privacy-preserving distributed ML scheme referred to as Federated learning (FL) enables generating a target model without sharing local information through on-device model training on edge resources. In this paper, we propose an FL-based application to predict customers’ financial issues by constructing a global learning model that is evolved based on the local models of the distributed agents. The local models are generated by the network agents using their ondevice data and local resources. We used the FL concept because the learning strategy does not require sharing any data with the server or any other agent that ensures the preservation of customers’ sensitive data. To that end, we enable partial works from the weak agents that eliminate the issue if the model convergence is retarded due to straggler agents. We also leverage asynchronous FL that cut off the extra waiting time during global model generation. We simulated the performance of our FL model considering a popular dataset, Give me Some Credit (Freshcorn, 2017). We evaluated our proposed method considering a a different number of stragglers and setting up various computational tasks (e.g., local epoch, batch size), and simulated the training loss and testing accuracy of the prediction model. Finally, we compared the F1-score of our proposed model with the existing centralized and decentralized approaches. Our results show that our proposed model achieves an almost identical F1-score as like centralized model even when we set up a skew-level of more than 80% and outperforms the state-of-the-art FL models by obtaining an average of 5 ∼ 6% higher accuracy when we have resource-constrained agents within a learning environment.

Recently, blockchain has received much attention from the mobilitycentric Internet of Things (IoT). It is deemed the key to ensuring the built-in integrity of information and security of immutability by design in the peer-to-peer network (P2P) of mobile devices. In a permissioned blockchain, the authority of the system has control over the identities of its users. Such information can allow an illintentioned authority to map identities with their spatiotemporal data, which undermines the location privacy of a mobile user. In this paper, we study the location privacy preservation problem in the context of permissioned blockchain-based IoT systems under three conditions. First, the authority of the blockchain holds the public and private key distribution task in the system. Second, there exists a spatiotemporal correlation between consecutive location-based transactions. Third, users communicate with each other through short-range communication technologies such that it constitutes a proof of location (PoL) on their actual locations. We show that, in a permissioned blockchain with an authority and a presence of a PoL, existing approaches cannot be applied using a plug-and-play approach to protect location privacy. In this context, we propose BlockPriv, an obfuscation technique that quantifies, both theoretically and experimentally, the relationship between privacy and utility in order to dynamically protect the privacy of sensitive locations in the permissioned blockchain.

Critical infrastructures (e.g., energy and transportation systems) are essential lifelines for most modern sectors and have utmost significance in our daily lives. However, these important domains can fail to operate due to system failures or natural disasters. Though the major disturbances in such critical infrastructures are rare, the severity of such events calls for the development of effective resilience assessment strategies to mitigate relative losses. Traditional critical infrastructure resilience approaches consider that the available critical infrastructure agents are resource-sufficient and agree to exchange local data with the server and other agents. Such assumptions create two issues: (1) uncertainty in reaching convergence while applying learning strategies on resource-constrained critical infrastructure agents, and (2) a huge risk of privacy leakage. By understanding the pressing need to construct an effective resilience model for resource-constrained critical infrastructure, this paper aims at leveraging a distributed machine learning technique called Federated Learning (FL) to tackle an agent’s resource limitations effectively and at the same time keep the agent’s information private. Particularly, this paper is focused on predicting the probable outage and resource status of critical infrastructure agents without sharing any local data and carrying out the learning process even when most of the agents are incapable of accomplishing a given computational task. To that end, an FL algorithm is designed specifically for a resource-constrained critical infrastructure environment that could facilitate the training of each agent in a distributed fashion, restrict them from sharing their raw data with any other external entities (e.g., server, neighbor agents), choose proficient clients by analyzing their resources, and allow a partial amount of computation tasks to be performed by the resource-constrained agents. We considered a different number of agents with various stragglers and checked the performance of FedAvg and our proposed FedResilience algorithm with prediction tasks for a probable outage, as well as checking the agents’ resource-sharing scope. Our simulation results show that if the majority of the FL agents are stragglers and we drop them from the training process, then the agents learn very slowly and the overall model performance is negatively affected. We also demonstrate that the selection of proficient agents and allowing them to complete only parts of their tasks can significantly improve the knowledge of each agent by eliminating the straggler effects, and the global model convergence is accelerated.