Federated learning (FL) is an emerging paradigm that enables multiple clients to collaboratively train a machine learning (ML) model without the need to exchange their raw data. However, it relies on a centralized authority to coordinate participants’ activities. This not only interrupts the entire training task in case of a single point of failure, but also lacks an effective regulatory mechanism to prevent malicious behavior. Although blockchain, with its decentralized architecture and data immutability, has significantly advanced the development of FL, it still struggles to withstand poisoning attacks and faces limitations in computational scalability. We propose Zkfhed, a verifiable and scalable FL system that overcomes the limitations of blockchain-based FL in poison attacks and computational scalability. First, we propose a two-stage audit scheme based on zero-knowledge proofs (ZKPs), which verifies that the training data are extracted from trusted organizations and that computations on the data exactly follow the specified training protocols. Second, we propose a homomorphic encryption delegation learning (HEDL), based on fully homomorphic encryption (FHE). It is capable of outsourcing complex computing to external computing resources without sacrificing the client's data privacy. Final, extensive experiments on real-world datasets demonstrate that Zkfhed can effectively identify malicious clients and is highly efficient and scalable in terms of online time and communication efficiency.

With the advent of Bitcoin, a cryptographically-enabled peer-to-peer digital payment system, blockchain together with a whole package of distributed ledger technologies, which serve as the underlying foundation of all the crypto-currencies, have been gaining attention from both academia and industry in the last fifteen years. The recent years have witnessed tremendous momentum in the development of blockchain and distributed ledger technologies, largely due to the impressive rise in the market capital of these digital tokens. More and more industries, from banking and insurance, to supply chain and e-commerce, are quickly realizing the great potential in blockchain technology in efficiency boost, process automation and secure data sharing across otherwise isolated data silos. Furthermore, as the recognition of the data value began to sink in, data assets has become an essential part of the development of enterprises and countries. Blockchain technology is regarded as the foundation of digital economy and provides an effective approach for data ownership, pricing and transactions, which are the core issues of data asset management. However, the potential implications of Blockchain technologies go far beyond their application as the technological backbone for cryptocurrencies. Web3.0, using blockchain as underlying technology, allow for various novel application scenarios, which are built upon distributed consensus and thus are hard to block or censor while providing public verifiability of peer-to-peer transactions without a trusted central party. Web3.0 are expected to become the main front for a plethora of highly expressive applications. To more thoroughly explore the potential of blockchain and web3.0 and promote their progress, SDBD'24 will provide a forum for the most recent blockchain and web3.0 research, innovations, and applications, bridging the gap between theory and practice in the design.

In the metaverse the physical space and the virtual space co-exist, and interact simultaneously. While the physical space is virtually enhanced with information, the virtual space is continuously refreshed with real-time, real-world information. To allow users to process and manipulate information seamlessly between the real and digital spaces, novel technologies must be developed. These include smart interfaces, new augmented realities, and efficient data storage, management, and dissemination techniques. In this paper, we first discuss some promising co-space applications. These applications offer opportunities that neither of the spaces can realize on its own. Then, we further discuss several emerging technologies that empower the construction of metaverse. After that, we discuss comprehensively the data centric challenges. Finally, we discuss and envision what are likely to be required from the database and system perspectives.

In the metaverse the physical space and the virtual space co-exist, and interact simultaneously. While the physical space is virtually enhanced with information, the virtual space is continuously refreshed with real-time, real-world information. To allow users to process and manipulate information seamlessly between the real and digital spaces, novel technologies must be developed. These include smart interfaces, new augmented realities, and efficient data storage, management, and dissemination techniques. In this paper, we first discuss some promising co-space applications. These applications offer opportunities that neither of the spaces can realize on its own. We then discuss challenges. Finally, we discuss and envision what are likely to be required from the database and system perspectives.

In adaptive learning system, the key to promote personalized learning is the learner model. The Elo model has great potential in online learning environment, so based on it, we propose the EELO which is an improved extension of Elo rating system, in view of polychotomously scored items and different granularity evaluations that the Elo rating system do not cover. The performance of the EELO estimating learners' abilities and predicting their future performances are evaluated on two large data set, which demonstrates that the EELO is better-performing. Then, we apply it in a real online learning environment to provide an analysis report for different user roles, which also can be used as the reference for the development of adaptive learning applications in the future.

Learning is an important activity from elementary school to university and longer (long-life learning). A variety of learning models and approaches are used with more or less active and autonomous orientations. The objective of technology-supported learning is to assist and empower these approaches by proposing Learning environments. Collaboration is a key aspect of learning in that it allows learners to share and exchange learning contributions. In group learning (class learning), size is often a problem for the teacher, making it hard for them to assist and supervise all learners. In this paper we propose a Collaborative Learning Environment using technology-supported tools organized as a system, taking into account different learning approaches. Class learners are dynamically split into smaller groups, which are either managed by the teacher (for learners needing close supervision and guidance) or by the Virtual Assistants - Chatbots (for more autonomous learners). This split is not only based on actual working results but also on learning emotion perception. We thus aim to combine pedagogy and a neuroscience-based view of emotions and emotional approaches. We provide the main principles, the system architecture, the orchestration process and the first results.

Work-based learning is a crucial approach to promoting professionals' working and learning efficiency. It is just-in-time, work-related, informal and spontaneous. With an aim to facilitate this kind of learning, in the paper we propose WoBaLearn, a work-based learning system, which can provide professionals with just-in-time, personalized and work-related learning supports. This paper specifies the design of a learning process in WoBaLearn based on a previously designed architecture. To evaluate the system, a learning scenario, in which professionals engage in a work-based learning activity with the support of WoBaLearn, is assumed and implemented. Results obtained from the scenario evaluate positively the design of WoBaLearn.

This article presents the results of a design workshop that developed scenarios of learning in an imaginary "smart city": Villard-de-Lans (Vercors, French Alps) by exploring suitable methodological approaches to the Smart City Learning Design. The aim of this case study was to propose "glocal" solutions intended to balance the global trends and the expectations of the community of reference (local elements), while distinguishing between access to information and participation in informal learning processes located in a smart city.

This paper proposes a framework for a professional context-aware mobile learning system. This system provides learning contents delivered via mobile devices and adapted to learning needs, personal characteristics and particular circumstances. Supported by this system, professionals will acquire personalized, just-in-time and problem-based learning contents in real working situations.