Reviewerly/exHarmony huggingface dataset:
Data-aware Hierarchical Federated Learning via Task Offloading

To cope with the high communication overhead caused by frequent aggregation of Federated Learning (FL) in Multi-access Edge Computing (MEC) scenarios, Hierarchical Federated Edge Learning (HFEL) is proposed as an evolving framework. HFEL offloads tasks to edge servers for partial model aggregation to reduce network traffic. However, most of the existing research focuses on resource optimization for HFEL without considering the impact of data characteristics and cannot guarantee the quality of FL training. To this end, we propose a task offloading approach based on data and resource heterogeneity under HFEL to improve training performance and reduce system cost. Specifically, we leverage information entropy to incorporate data statistical features into the cost function to reshape edge datasets. In addition, we applied Multi-Agent Deep Deterministic Policy Gradient (MADDPG) with a resource allocation module to generate distributed offloading policy more efficiently. Our algorithm not only adopts local observations to obtain the optimal action but also takes into account device heterogeneity, which can adapt to the unstable edge environment. Extensive experiments under multiple datasets and baselines are carried out, which demonstrate that our algorithm can effectively improve the accuracy of aggregated models while reducing system cost.
RF Fingerprinting Needs Attention: Multi-task Approach for Real-World WiFi and Bluetooth

A novel cross-domain attentional multi-task architecture - xDom - for robust real-world wireless radio frequency (RF) fingerprinting is presented in this work. To the best of our knowledge, this is the first time such comprehensive attention mechanism is applied to solve RF fingerprinting problem. In this paper, we resort to real-world IoT WiFi and Bluetooth (BT) emissions (instead of synthetic waveform generation) in a rich multipath and unavoidable interference environment in an indoor experimental testbed. We show the impact of the time-frame of capture by including waveforms collected over a span of months and demonstrate the same time-frame and multiple time-frame fingerprinting evaluations. The effectiveness of resorting to a multi-task architecture is also experimentally proven by conducting single-task and multi-task model analyses. Finally, we demonstrate the significant gain in performance achieved with the proposed xDom architecture by benchmarking against a well-known state-of-the-art model for fingerprinting. Specifically, we report performance improvements by up to 59.3% and 4.91x under single-task WiFi and BT fingerprinting respectively, and up to 50.5% increase in fingerprinting accuracy under the multi-task setting.
Propagable Backdoors over Blockchain-based Federated Learning via Sample-Specific Eclipse

Blockchain-based federated learning, also being named as swarm learning, is perceived to have great potential to support decentralized and privacy-enhancing big data processing. However, numerous serious vulnerabilities found on blockchain and federated learning enforce us to concern about the security of swarm learning. Some seemingly-unrelated combinations of known vulnerabilities may derive highly-converted and unknown threats to swarm learning. In this paper, we first investigate the security threats of the swarm learning framework. And then, leveraging backdoor attacks and eclipse attacks, a novel hybrid vulnerability that can furtively propagate backdoors among swarm learning nodes is identified. To speed up the backdoor propagation and reduce attack costs, a sample-specific eclipse (SSE) strategy that can select the swarm network node with a high data contribution rate as the attack object is also proposed. Finally, by adjusting the trigger size, the data distribution rate, and the poisoning ratio, we conduct various comparison experiments to validate the feasibility of the proposed methods. To the best of our knowledge, this is the first article to study the epidemicity of backdoors in swarm learning.
On the Design of Communication-Efficient Federated Learning for Health Monitoring

With the booming deployment of Internet of Things, health monitoring applications have gradually prospered. Within the recent COVID-19 pandemic situation, interest in permanent remote health monitoring solutions has raised, targeting to reduce contact and preserve the limited medical resources. Among the technological methods to realize efficient remote health monitoring, federated learning (FL) has drawn particular attention due to its robustness in preserving data privacy. However, FL can yield to high communication costs, due to frequent transmissions between the FL server and clients. To tackle this problem, we propose in this paper a communication-efficient federated learning (CEFL) framework that involves clients clustering and transfer learning. First, we propose to group clients through the calculation of similarity factors, based on the neural networks characteristics. Then, a representative client in each cluster is selected to be the leader of the cluster. Differently from the conventional FL, our method performs FL training only among the cluster leaders. Subsequently, transfer learning is adopted by the leader to update its cluster members with the trained FL model. Finally, each member fine-tunes the received model with its own data. To further reduce the communication costs, we opt for a partial-layer FL aggregation approach. This method suggests partially updating the neural network model rather than fully. Through experiments, we show that CEFL can save up to to 98.45% in communication costs while conceding less than 3% in accuracy loss, when compared to the conventional FL. Finally, CEFL demonstrates a high accuracy for clients with small or unbalanced datasets.
Energy Efficient Federated Learning over Cooperative Relay-Assisted Wireless Networks

Federated learning (FL) is a promising distributed learning paradigm, which can effectively avoid the privacy leakage and communication issues compared with the centralized learning. Specifically, in each training iteration, FL nodes only upload the local training results to the centralized server without disclosure of their raw training dataset and the centralized server will aggregate the local results of all FL nodes and update the global model. To this end, the performance of the global model is highly dependent on the nodes' cooperation. However, it is challenging to motivate mobile edge devices to involve themselves in the FL process without a desired incentive. Another significant concern of the mobile edge devices is the communication and computational energy cost of participation. Therefore, considering the high cost and weak communication channel with the centralized server specially for the distant nodes, in this paper, we propose a relay-assisted energy efficient scheme for federated learning, where each FL computational node is not only motivated by monetary awards based on their local dataset, but also further motivated to function as a relay node to assist distant nodes on local results uploading due to its locality advantage. To achieve a stable pairing solution between FL computational nodes and assisted relays in a distributive fashion, a many-to-one matching algorithm is applied, where each the computational node and relay is unable to deviate with current pairing unilaterally for higher revenue. Extensive simulations are conducted to illustrate the correctness and effectiveness of our proposed scheme.
WeVoting: Blockchain-based Weighted E-Voting with Voter Anonymity and Usability

E-voting plays a vital role in guaranteeing and promoting social fairness and democracy. However, traditional e-voting schemes rely on a centralized organization, leading to a crisis of trust in the vote-counting results. In response to this problem, researchers have introduced blockchain to realize decentralized e-voting, but the adoption of blockchain also brings new issues in terms of flexibility, anonymity, and usability. To this end, in this paper, we propose WeVoting, which provides weight-based flexibility with solid anonymity and enhances usability by designing a voter-independent on-chain counting mechanism. Specifically, we use distributed ElGamal homomorphic encryption and zero-knowledge proof to achieve voting anonymity with weight. Besides, WeVoting develops a counter-based counting mechanism to enhance usability compared with those self-tallying schemes. By critically designing an honesty-and-activity-based incentive algorithm, WeVoting can guarantee a correct counting result even in the presence of malicious counters. Our security and performance analyses elaborate that WeVoting achieves high anonymity in weighed voting under the premise of meeting the basic security requirements of e-voting. And meanwhile, its counting mechanism is sufficient for practical demands with reasonable overheads.
Robust and Efficient Modulation Recognition with Pyramid Signal Transformer

A robust and efficient pyramid signal Transformer model, called SigFormer for automatic modulation recognition was proposed in this paper. In SigFormer, a pyramid Transformer architecture is introduced to encode the relationship between the internal features of modulated signals. Specifically, a dual-attention block composed of self-attention layer and scaling-attention layer is proposed for simultaneous global feature repre-sentation and noise resistance learning for modulated signals, and small-kernel convolution layers embedded to dual-attention block and feed-forward block is proposed for fine-grained modulation recognition as well. Experiments on RML2018.01a, RML2016.10a and RML2016.10b show that the SigFormer outperformed most other deep learning models on recognition accuracy, and it is more parameter-efficient than most other models and more robust on low signal-to-noise ratio (SNR) signals.
Blockchain-based 6G Inter-Provider Agreements: Auction vs. Marketplace

Blockchain and Distributed Ledger Technologies (DLT) can create an agreement that enables stakeholders in the telco world to interact and share services while maintaining trust. As a result, the multiple 6G network stakeholders can perform a role in a marketplace as service providers or consumers. Blockchain and DLT have immutability, transparency, and decentralized ledgers, making them critical in a future-generation network where various administrative domains are controlled by separate operators and resource providers to share resources (virtual network functions, services, or slices). Similarly, an auction-based approach based on the blockchain can eliminate intermediaries while ensuring transparency to increase competitiveness in such an environment. Therefore, in this paper, we propose a solution based on blockchain, considering both approaches (i.e., marketplace and auction). Furthermore, we use IOTA Tangle and IPFS to reduce the transaction and gas costs, which is one of the challenges for Ethereum-based use cases. We also provide experimental evaluations of our use case to analyze the results. It is observed that both options expand the consumer's possibilities to choose, and creating smart contracts adds trust. Moreover, the potential advantages of the proposed solutions are demonstrated through emulation results as a comparison to the traditional ways of doing such agreements between inter-provider domains. We measure transaction latency and the maximum latency observed in our proposed approach is ≈ 25ms. Similarly, the average response time for the Ethereum-based approach is double our proposed approach's average response time. Consequently, the services are completed relatively fast with less latency and faster response time. Moreover, it is observed that in ≈ 2000s, up to 30 services can be completed in a marketplace approach and up to 20 ~ 25 services in the auction. Lastly, the results also explain that by using IOTA-EVM, we can have feeless transactions, and IPFS helps reduce the storage cost up to ≈ 80%.
Synthetic Traffic Generation with Wasserstein Generative Adversarial Networks

Network traffic data are critical for network research. With the help of synthetic traffic, researchers can readily generate data for network simulation and performance evaluation. However, the state-of-the-art traffic generators are either too simple to generate realistic traffic or require the implementation of original applications and user operations. We propose Synthetic PAcket Traffic Generative Adversarial Networks (SPATGAN) that are capable of generating synthetic traffic. The framework includes a server agent and a client agent, which transmit synthetic packets to each other and take the opponent's synthetic packets as conditional labels for the built-in Timing Synthesis Generative Adversarial Networks (TSynGAN) and a Packet Synthesis Generative Adversarial Networks (PSynGAN) to generate synthetic traffic. The evaluations demonstrate that the proposed framework can generate traffic whose distribution resembles real traffic distribution.
XOR-Based Binary AEFA: Theoretical Studies and Applications

The Binary Artificial Electric Field Algorithm (B-AEFA) is the binary version of the Artificial Electric Field Algorithm (AEFA) scheme. In this article, two transfer functions are proposed to convert the real-valued candidate solutions of the original AEFA into binary values. Additionally, an XOR operator is used to enhance the effectiveness and wider applicability of the AEFA scheme in the binary format. This article also presents a theoretical analysis of the proposed work that makes recommendations for the conditions in which the velocity term of AEFA assumes the values in the positive and negative direction. Furthermore, the proposed scheme is used to solve real-world feature selection problems. The proposed scheme gives effective results that are superior to other competent algorithms in most cases.
Normalized Post-training Quantization for Photonic Neural Networks

The recent advances of Deep Learning (DL) have fueled the research of hardware accelerators, which can signif-icantly improve the computation speed and energy efficiency. Neuromorphic photonics are among the most promising emerging approaches, as they can operate at very high frequencies with minimal energy and power consumption. At the same time, such analog architectures also impose a number of limitations arising from noise sources, digital-to-analog (DAC), and analog-to-digital (ADC) conversions that lead to reducing the effective bit resolution for DL models. The main contribution of this work is a photonic-compliant post-training quantization method that effectively allows us to deploy limited precision models without significant performance degradation, enabling us to reduce the cost and complexity of photonic substrates. The proposed method is capable of effectively handling outliers and equally distributing the quantized parameters, improving the accuracy of DL models even when compared with more computationally-intensive methods, such as quantization-aware training approaches. The effectiveness of the proposed method is demonstrated in Convolutional Neural Networks (CNNs), employing different photonic configurations, lowering the models' precision requirements down to 4-bits.
Integrity Auditing for Secure Cloud Storage on Sensitive Data Protection

Users can interchange data with others and remotely store their data on the cloud using cloud storage services. The integrity of data saved in the cloud should be ensured through remote data integrity audits. An electronic health record system is one popular example of a well-known cloud storage system where a cloud file may include unique sensitive data. When a cloud file is shared, private information shouldn’t be exposed. Sensitive information can be realized if the entire shared file is encrypted, but other users cannot access it. How to share sensitive material that is hidden in remote data integrity audits is still up in the air. In this method, data blocks that match sensitive information in the file are sanitized, and the data block signatures are changed to ones that are appropriate for the cleaned-up file. Throughout the integrity auditing process, these signatures are used to verify the accuracy of the cleaned file. Since sensitive information is concealed, our solution permits the sharing and usage of a cloud-stored file by others while maintaining the capacity to conduct remote data integrity audits efficiently.
EET-Hamming monocular high-speed measurement for long-span bridge structure displacement on a shaking table

Monocular displacement measurement is widely used for the vibration monitoring of bridge structures on shaking tables. To efficiently and robustly measure the low-contrast targets obtained by a high-speed camera, this study proposes an EET-Hamming monocular high-speed measurement method. First, the EET and Otsu methods are combined to enhance and binarize the obtained image sequences, which can track the obtained low-contrast targets efficiently and robustly. Second, the minimum Hamming distance matching method is proposed to determine the similarity of the images and further improve the efficiency and accuracy of the image sequence processing. Finally, a nine-point least squares polynomial surface fitting method is proposed to calculate the sub-pixel displacement and convert them to the corresponding actual displacement. The results show that the proposed EET-Hamming monocular high-speed measurement method has a higher displacement measurement accuracy and processing efficiency.
FBL-ET: A federated broad learning framework based on event trigger

Federated learning (FL) is proposed to exchange models between server and clients rather than data, which protects privacy. But one important problem in FL is that the clients may be unreliable. The clients may suddenly hang up. If the hung-up clients are selected by the server, the time consumption can be significant. To solve this problem, in this paper, the clients are set to have the communication initiative, so they can decide whether or not to upload their local models. Another problem in FL is that the quality of the clients’ models cannot be determined. If some models have bad quality, they will have a bad impact on the quality of the global model. To improve the quality of models upload by clients, we set an upload condition for each client, and the client will upload the model only when the condition is satisfied. For both purposes, we introduce an event-triggered method into federated broad learning, i.e. FBL-ET. We set up a test set on each client, and based on the prediction accuracy, a specified dynamic trigger condition is designed for each client. Therefore, FBL-ET reduces time consumption and improves the quality of uploaded models by introducing an event-triggered mechanism. In addition, in order to improve the activity of clients and attract more high-quality clients to participate in global aggregation, FBL-ET introduces an incentive mechanism. Finally, experimental results show that FBL-ET has higher accuracy than the original federated broad learning method, and can effectively reduce the waste of communication consumption.
Self-supervised Pretraining for 2D Medical Image Segmentation

Supervised machine learning provides state-of-the-art solutions to a wide range of computer vision problems. However, the need for copious labelled training data limits the capabilities of these algorithms in scenarios where such input is scarce or expensive. Self-supervised learning offers a way to lower the need for manually annotated data by pretraining models for a specific domain on unlabelled data. In this approach, labelled data are solely required to fine-tune models for downstream tasks. Medical image segmentation is a field where labelling data requires expert knowledge and collecting large labelled datasets is challenging; therefore, self-supervised learning algorithms promise substantial improvements in this field. Despite this, self-supervised learning algorithms are used rarely to pretrain medical image segmentation networks. In this paper, we elaborate and analyse the effectiveness of supervised and self-supervised pretraining approaches on downstream medical image segmentation, focusing on convergence and data efficiency. We find that self-supervised pretraining on natural images and target-domain-specific images leads to the fastest and most stable downstream convergence. In our experiments on the ACDC cardiac segmentation dataset, this pretraining approach achieves 4–5 times faster fine-tuning convergence compared to an ImageNet pretrained model. We also show that this approach requires less than five epochs of pretraining on domain-specific data to achieve such improvement in the downstream convergence time. Finally, we find that, in low-data scenarios, supervised ImageNet pretraining achieves the best accuracy, requiring less than 100 annotated samples to realise close to minimal error.
HR Digital Transformation: Blockchain for Business

India is striving very hard to establish in today’s competitive world. We need to be ahead in our socio-cultural, socio-psychological, political and economic business scenario. We need to act very fast in digitalization of business processes and practices. People who are regarded as an employee are the human resources and are also the epitome of both of private and public sector organizations. By the passage of time, these human resources act in many fields such as in e-governance, insurance sector, sourcing and procurement, healthcare, education, supply chain, banking, real-estate, logistics, tourism and hospitality, and cyber security. Human resources showed tremendous efforts and contributions in attainment of the organizational goals and objectives. Simultaneously received tremendous pain and sorrow when betrayed, cheated, disturbed by the corrupted system. When the evolution of blockchain technology is being hailed as the second-generation internet which is a game-changer have changed the entire business scenario being completely decentralized and totally transparent. The motivation behind the study is that people for the first time in human history can trust and transact with each other in a much-secured manner. The emergence of blockchain technology could further transform the world of HR. This study has explored the concept of digitalization, human resource, and blockchain technology from an extensive literature survey. The purpose of this study is to explore the concept of blockchain technology and its relevance in today’s competitive businesses. The study also emphasizes on different characteristics and the importance of blockchain technology in varied HR practices. This chapter has attempted to discuss certain research questions such as (a) what is human resource and why human resource? (b) What is digital transformation? (c) What is blockchain? (d) Why blockchain? (e) What are the various types of blockchain? (f) Why blockchain in HR? (g) How blockchain has transformed HR? (h) How far blockchain is relevant for HR? and (i) HR digital transformation vis-à-vis blockchain is examined in this study along with the challenges and criticisms. This paper highlights the countries and companies who have already formulated, implemented, and executed blockchain technology in their work places for higher productivity gains and to be recognized as digitalized country in global scenario. Hence, the main results of the current study focused on the HR digital transformation considering blockchain for businesses. This study will help HR practitioners to think out of the box and thereby implement blockchain technology for the beneficial of the organization as well as to all the internal and external stakeholders and society at large. This study will also help in cost reduction in an effective manner. Therefore, it becomes inevitable to focus on the HR digital transformation considering blockchain for businesses to remain sustainable.