Xiao Li

Federated learning (FL) is emerging as a sought-after distributed machine learning architecture, offering the advantage of model training without direct exposure of raw data. With advancements in network infrastructure, FL has been seamlessly integrated into edge computing. However, the limited resources on edge devices introduce security vulnerabilities to FL in the context. While blockchain technology promises to bolster security, practical deployment on resource-constrained edge devices remains a challenge. Moreover, the exploration of FL with multiple aggregators in edge computing is still new in the literature. Addressing these gaps, we introduce the Blockchain-empowered Heterogeneous Multi-Aggregator Federated Learning Architecture (BMA-FL). We design a novel light-weight Byzantine consensus mechanism, namely PBCM, to enable secure and fast model aggregation and synchronization in BMA-FL. We also dive into the heterogeneity problem in BMA-FL that the aggregators are associated with varied number of connected trainers with Non-IID data distributions and diverse training speed. We propose a multi-agent deep reinforcement learning algorithm to help aggregators decide the best training strategies. The experiments on real-word datasets demonstrate the efficiency of BMA-FL to achieve better models faster than baselines, showing the efficacy of PBCM and proposed deep reinforcement learning algorithm.

Blockchain technology, recognized for its decentralized and privacy-preserving capabilities, holds potential for enhancing privacy in contact tracing applications. Existing blockchain-based contact tracing frameworks often overlook one or more critical design details, such as the blockchain data structure, a decentralized and lightweight consensus mechanism with integrated tracing data verification, and an incentive mechanism to encourage voluntary participation in bearing blockchain costs. Moreover, the absence of framework simulations raises questions about the efficacy of these existing models. To solve above issues, this article introduces a fully third-party independent blockchain-driven contact tracing (BDCT) framework, detailed in its design. The BDCT framework features an Rivest-Shamir-Adleman (RSA) encryption-based transaction verification method (RSA-TVM), achieving over 96% accuracy in contact case recording, even with a 60% probability of individuals failing to verify contact information. Furthermore, we propose a lightweight reputation corrected delegated proof of stake (RC-DPoS) consensus mechanism, coupled with an incentive model, to ensure timely reporting of contact cases while maintaining blockchain decentralization. Additionally, a novel simulation environment for contact tracing is developed, accounting for three distinct contact scenarios with varied population density. Our results and discussions validate the effectiveness, robustness of the RSA-TVM and RC-DPoS, and the low storage demand of the BDCT framework.

Bitcoin has became one of the most popular investment asset recent years. The volatility of bitcoin price in financial market attracting both investors and researchers to study the price changing manners of bitcoin. Existing works try to understand the bitcoin price change by manually discovering features or factors that are assumed to be reasons of price change. However, the trivial feature engineering consumes human resources without the guarantee that the assumptions or intuitions are correct. In this paper, we propose to reveal the bitcoin price change through understanding the patterns of bitcoin blockchain transactions without feature engineering. We first propose k-order transaction subgraphs to capture the patterns. Then with the help of machine learning models, Multi-Window Prediction Framework is proposed to learn the relation between the patterns and the bitcoin prices. Extensive experimental results verify the effectiveness of transaction patterns to understand the bitcoin price change and the superiority of Multi-Window Prediction Framework to integrate multiple submodels trained separately on multiple history periods.

Blockchain is an emerging decentralized data collection, sharing, and storage technology, which have provided abundant transparent, secure, tamper-proof, secure, and robust ledger services for various real-world use cases. Recent years have witnessed notable developments of blockchain technology itself as well as blockchain-enabled applications. Most existing surveys limit the scopes on several particular issues of blockchain or applications, which are hard to depict the general picture of current giant blockchain ecosystem. In this paper, we investigate recent advances of both blockchain technology and its most active research topics in real-world applications. We first review the recent developments of consensus and storage mechanisms and communication schema in general blockchain systems. Then extensive literature review is conducted on blockchain-enabled Internet of Things (IoT), edge computing, federated learning, and several emerging applications including healthcare, COVID-19 pandemic, online social network, and supply chain, where detailed specific research topics are discussed in each. Finally, we discuss the future directions, challenges, and opportunities in both academia and industry.

In dynamic networks, links are annotated with timestamps showing the emerging time and the link prediction problem is to infer the future links in networks. Universal link prediction methods are highly demanded in various applications, which require universal link features that are feasible for multiple kinds of network topological structures and capable to address the difference of links with different timestamps. In this paper, we propose a novel link feature called Structure Subgraph Feature (SSF). The SSF is an outstanding link feature that is feasible to various dynamic networks due to the following superiorities: (1) the proposed structure subgraph is so far the most effective manner to represent surrounding topological features of target link and (2) the normalized influence well specifies the influence of multiple links and different timestamps in structure subgraph. We finally propose two link prediction methods by applying SSF to a linear regression model and a neural machine. Experimental results on real-world dynamic network datasets indicate that the SSF-based methods consistently provide top-class performance on various dynamic networks.