Марат Рашитович Сафиуллин, Leonid Alekseevich Elshin, Yaroslav Kuznetsov
Objective: This study seeks to substantiate the prospects for using blockchain technologies as a mechanism to attract Islamic finance to the Russian regions, with the dual aim of mitigating sanctions-related restrictions and fostering integration into global Islamic financial ecosystems. Methodology/Approach: The research employs econometric and systems analysis to assess the macroeconomic externalities of blockchain-driven Islamic finance inflows. A methodological toolkit was developed and tested to estimate potential market capacity, using data from four Russian regions (Tatarstan, Bashkortostan, Chechnya, Dagestan) through 2030. The approach incorporates substitution modeling of lost Western capital, scenario analysis, and the application of blockchain-based financial gateways. Originality/Relevance: The originality of this work lies in linking two underexplored areas—Islamic finance and blockchain technologies—in the context of Russia’s geoeconomic reorientation toward Asia and the Global South. The study provides an innovative framework for replacing Western capital flows with investments from Islamic finance markets through decentralized fintech solutions. Main Conclusion: Findings demonstrate that the use of blockchain-based financial mechanisms can significantly expand the capacity of Russian regions to attract Islamic finance. Tatarstan and Bashkortostan show the highest potential, while Chechnya and Dagestan present smaller but strategically relevant capacities. Blockchain solutions are positioned as a breakthrough tool for overcoming international financial isolation and enabling long-term convergence with Islamic digital ecosystems. Theoretical/Methodological Contribution: The study advances the methodological basis for assessing fintech’s role in regional investment attraction by introducing a quantitative model that integrates substitution coefficients, market capitalization ratios, and penetration indices. It enriches the theoretical discourse on blockchain’s economic externalities and provides policymakers and practitioners with actionable instruments for embedding Islamic finance within regional development strategies.
Cryptocurrency – a digital currency based on decentralized blockchain technology has been controversial since the creation of the first cryptocurrency, Bitcoin in 2009. While some people believe that it brings a revolutionary technological tool which has the potential to challenge the existing financial architecture or even destroy it all together, others consider it as a classic bubble created by hyped expectations. This review aims at explaining the technique that forms the basis of cryptocurrencies, how they have been implemented in different industries, and the advantages and disadvantages of the technology. Topics include financial accessibility, transaction velocity, and emergence of decentralised finance, notable issues involve unpredictability, the lack of clarity concerning regulation, and concerns about the effects on the environment. Lastly, the paper concludes whether introducing the cryptocurrency is revolutionary in the field of finance or just another fad.
Bitcoin transaction anomaly detection is essential for maintaining financial market stability. A significant challenge is capturing the dynamically evolving transaction patterns within transaction networks. Dynamic graph models are effective for characterizing the temporal evolution of transaction systems. However, current methods struggle to mine long-range temporal dependencies and address the class imbalance caused by the scarcity of abnormal samples. To address these issues, we propose a novel approach, the Bidirectional EvolveGCN with Class-Balanced Learning Network (Balanced-BiEGCN), for Bitcoin transaction anomaly detection. This model integrates two key components: (1) a bidirectional temporal feature fusion mechanism (Bi-EvolveGCN) that enhances the capture of long-range temporal dependencies and (2) a Sample Class Transformation (CSCT) classifier that generates difficult-to-distinguish abnormal samples to balance the positive and negative class distribution. The generation of these samples is guided by two loss functions: the adjacency distance adaptive loss function and the symmetric space adjustment loss function, which optimize the spatial distribution and confusion of abnormal samples. Experimental results on the Elliptic dataset demonstrate that Balanced-BiEGCN outperforms existing baseline methods in anomaly detection.
Smart contracts—auto-executing digital agreements built on DLT (Distributed Ledger Technology), an emerging technology of blockchain—are revolutionizing cross-border payments by enhancing efficiency and automation. However, their widespread adoption is hindered by a fragmented regulatory landscape and legal uncertainties across jurisdictions. Therefore, to promote the urgency of regulatory governance of smart contract, this research advocates for the techno-legal standardization of smart contracts to ensure regulatory compliance in international financial transactions. It investigates how smart contracts can be designed to meet diverse legal requirements while maintaining technical adaptability, scalability, and interoperability. Drawing on interdisciplinary literature and qualitative methods—including expert interviews, surveys, and case studies—the study aims to develop a framework that balances innovation with legal certainty. Key challenges addressed include jurisdictional fragmentation, enforcement mechanisms, integration with legacy systems like SWIFT, and compliance with KYC/AML regulations. The research also examines emerging solutions such as decentralized identity frameworks, trusted oracles, and hybrid on-chain/off-chain models. By bridging the gap between law, technology, and finance, this study offers actionable insights for policymakers, financial institutions, blockchain developers, and international businesses. Ultimately, it contributes to the development of a standardized smart contract ecosystem that supports secure, efficient, and legally compliant cross-border payments.
Rabbiya Younas, Hafiz Muhammad Raza Ur Rehman, Gyu Sang Choi
Cryptocurrencies function as a digital exchange medium operating on network-based technology, where records are secured using cryptographic algorithms such as Secure Hash Algorithm 2 (SHA-2) and Message Digest 5 (MD5). These cryptocurrencies utilize blockchain technology to provide transparent, reliable, and immutable transactions. Consequently, cryptocurrencies have gained significant traction across multiple sectors, particularly finance. However, their value is still prone to considerable fluctuations, which raises concerns about the risks associated with investments. The emerging discipline of cryptocurrency forecasting has gained popularity worldwide, and academics are employing a variety of deep learning (DL) and machine learning (ML) techniques to investigate the elements that influence cryptocurrency values. Among the various DL methods, LSTM has demonstrated noteworthy efficiency. Nevertheless, there are intrinsic downsides to LSTM, notably due to its sequential nature, which hinders parallelization and complicates the modeling of both short- and long-term dependencies. To address these shortcomings, the Transformer architecture has emerged as a potent solution. The Transformer is widely used in DL for its exceptional parallelization capabilities and its capacity to extract broad, distant data dependencies. Recent studies have explored Transformer-based approaches for cryptocurrency price forecasting, particularly for modeling long-term dependencies. However, these models often exhibit limitations in capturing high-frequency, short-term fluctuations, making them less suitable for short-term prediction tasks. Our proposed methodology introduces a novel Transformer-based hybrid framework designed to enhance forecasting accuracy across various time scales. We evaluate the forecasting accuracy for 10 cryptocurrencies at hourly, daily, and yearly frequencies. The findings show that, in comparison to other DL techniques such as LSTM, RNN, and baseline Autoformer, our model achieves superior accuracy. Furthermore, we benchmark our method against prominent Transformer variants such as Informer and FEDformer, and observe improved performance in both short- and long-term forecasting scenarios. These results indicate that our proposed model consistently outperforms existing state-of-the-art Transformer-based approaches in cryptocurrency price prediction.
Cha, Jinho, Long Hoang Pham, Thi Quynh Trang Vo, Jaeyoung Cho · 5 authors
This study develops and analyzes an optimization model of smart contract adoption under bounded risk, linking structural theory with simulation and real-world validation. We examine how adoption intensity alpha is structurally pinned at a boundary solution, invariant to variance and heterogeneity, while profitability and service outcomes are variance-fragile, eroding under volatility and heavy-tailed demand. A sharp threshold in the fixed cost parameter A3 triggers discontinuous adoption collapse (H1), variance shocks reduce profits monotonically but not adoption (H2), and additional results on readiness heterogeneity (H3), profit-service co-benefits (H4), and distributional robustness (H5) confirm the duality between stable adoption and fragile payoffs. External validity checks further establish convergence of sample average approximation at the canonical O(1/sqrt(N)) rate (H6). Empirical validation using S&P 500 returns and the MovieLens100K dataset corroborates the theoretical structure: bounded and heavy-tailed distributions fit better than Gaussian models, and profits diverge across volatility regimes even as adoption remains stable. Taken together, the results demonstrate that adoption choices are robust to uncertainty, but their financial consequences are highly fragile. For operations and finance, this duality underscores the need for risk-adjusted performance evaluation, option-theoretic modeling, and distributional stress testing in strategic investment and supply chain design.
In this report, we compare the performance of our UltraGroth-based zero-knowledge machine learning framework Bionetta to other tools of similar purpose such as EZKL, Lagrange's deep-prove, or zkml. The results show a significant boost in the proving time for custom-crafted neural networks: they can be proven even on mobile devices, enabling numerous client-side proving applications. While our scheme increases the cost of one-time preprocessing steps, such as circuit compilation and generating trusted setup, our approach is, to the best of our knowledge, the only one that is deployable on the native EVM smart contracts without overwhelming proof size and verification overheads.
This study tackles the computational challenges of solving Markov Decision Processes (MDPs) for a restricted class of problems. It is motivated by the Last Revealer Attack (LRA), which undermines fairness in some Proof-of-Stake (PoS) blockchains such as Ethereum (\$400B market capitalization). We introduce pseudo-MDPs (pMDPs) a framework that naturally models such problems and propose two distinct problem reductions to standard MDPs. One problem reduction provides a novel, counter-intuitive perspective, and combining the two problem reductions enables significant improvements in dynamic programming algorithms such as value iteration. In the case of the LRA which size is parameterized by $κ$ (in Ethereum's case $κ$= 325), we reduce the computational complexity from $O(2^κκ^{2^{κ+2}})$ to $O(κ^4)$ (per iteration). This solution also provide the usual benefits from Dynamic Programming solutions: exponentially fast convergence toward the optimal solution is guaranteed. The dual perspective also simplifies policy extraction, making the approach well-suited for resource-constrained agents who can operate with very limited memory and computation once the problem has been solved. Furthermore, we generalize those results to a broader class of MDPs, enhancing their applicability. The framework is validated through two case studies: a fictional card game and the LRA on the Ethereum random seed consensus protocol. These applications demonstrate the framework's ability to solve large-scale problems effectively while offering actionable insights into optimal strategies. This work advances the study of MDPs and contributes to understanding security vulnerabilities in blockchain systems.
This paper presents a privacy-preserving intrusion detection architecture tailored for smart home environments, addressing the dual challenge of maintaining data confidentiality while enabling accurate anomaly detection. The proposed system replaces conventional raw data analysis with a proof-driven mechanism leveraging Zero-Knowledge Proofs (ZKPs). Behavioral patterns from smart devices such as motion sensors, door contacts, and environmental monitors are abstracted into cryptographic representations, which are then processed by a zk-SNARK-compatible machine learning model. Inference results are accompanied by cryptographic proofs verifying the correctness of each decision without disclosing the input data. A private blockchain layer, implemented using Ethereum smart contracts, records event hashes, proof metadata, and decision outcomes to ensure tamper-evident logging and automated response handling. Experimental simulations on synthetic home automation datasets demonstrate that the architecture achieves over 92% anomaly detection accuracy while ensuring zero exposure of raw sensor streams. The system also exhibits low-latency proof generation (~400 ms) and end-to-end response time under 1.2 seconds, confirming its suitability for real-time smart home applications.
Decentralized autonomous organizations (DAOs) represent an innovation in the design of organizations by creating blockchain-based human-machine systems that are governed based on the collective decisions of their participants. Although this new form of organizing promises to sustain participation and foster decentralized governance, many existing DAOs have failed to achieve the intended degrees of decentralization. This study aims to understand how DAOs can fulfill their potential for decentralization by empowering individuals to participate in governance. Using an abductive approach guided by the empowerment theory, this research identifies three key practices underpinning empowerment in DAOs: promoting autonomy, ensuring transparency, and fostering communication. A configurational approach is used to identify complementarities among these practices that lead to three distinct governance archetypes associated with varying degrees of decentralization. Based on fuzzy-set qualitative comparative analysis (fsQCA) of 30 DAO cases, we introduce “deliberative democracy” as a DAO governance archetype that allows for increasingly decentralized governance. Our findings demonstrate that, although a high degree of autonomy is needed to sustain decentralization, there needs to be sufficient communication among autonomous actors to facilitate the collective management of DAOs. These findings advance the understanding of decentralization in information systems research and highlight the governance mechanisms that foster decentralization in blockchain-based systems.
As a data-free knowledge transfer paradigm, federated learning (FL) provides a novel solution for knowledge fusion in smart cities, especially in the field of Internet of Vehicles (IoV). However, the bandwidth bottleneck in the IoV limits the efficiency of federated collaboration, while trust issues associated with aggregation servers reduce users’ willingness to collaborate. To address these challenges, this article proposes a multiple collaborative iterative distillation-based swarm learning (CISL) framework for IoV. CISL leverages multiple collaborative iterative distillations to transform federated collaboration into serverless cross-device and cross-decentralized autonomous organization (DAO) knowledge transfer and fusion, enabling trustworthy swarm collaboration under bandwidth-constrained conditions. Moreover, it adaptively adjusts the inheritance and elimination of shared knowledge (SK) to enhance model adaptability and improve single-vehicle performance. Specifically, CISL proposes a collaborative iterative distillation mechanism that progressively integrates knowledge of other vehicles within the DAO, achieving cross-device SK fusion. Meanwhile, CISL introduces a multisage collaborative distillation mechanism, enabling each DAO to collaboratively distill and integrate SK from other DAOs, thereby expanding its knowledge domain. Additionally, CISL employs a dynamic balancing strategy to adaptively regulate the inheritance and elimination of SK, optimizing local models and enhancing their performance. Comprehensive experiments conducted on six benchmarks across two scenarios demonstrate that, compared to state-of-the-art methods, CISL exhibits superior adaptability and robustness across different datasets and task scenarios.
CALIVERSE is a next-generation AI metaverse platform that seamlessly combines immersive shopping, live performances, gaming, and Web3 user-generated content into a unified virtual experience. Built on cutting-edge technologies such as Unreal Engine 5, AI-powered lighting, VR AI enhancement, real time live-action rendering integration, and 3D AI conversion, CALIVERSE delivers an incredibly lifelike virtual world. The platform supports multiple environments, including PC, VR HMD, and glasses-free 3D displays (Screen Protector), delivering high-fidelity 4K+ virtual experiences with premium quality and deep immersion. In particular, CALIVERSE seamlessly composites live-action footage with real-time rendered graphics and utilizes AI lighting and image enhancement technologies to deliver natural and visually coherent virtual performances. It can depict audiences of over 80,000, setting it apart from conventional metaverse platforms designed for younger users. By integrating Korea's advanced server networking technology, CALIVERSE enables massively scaled simultaneous connections, setting a new standard for ultra-immersive next generation metaverse platforms.
Iswanda F. Satibi, Maureen A. Leksmana, Dini Pasha Ramadhani
This paper explores the alignment between DAO governance mechanisms, IT governance frameworks, and Indonesia’s regulatory environment. Using a qualitative document analysis of academic literature, DAO governance documentation, IT governance standards (COBIT, ITIL, ISO/IEC 38500), and Indonesian regulatory texts issued by Bappebti, Bank Indonesia, and the Financial Services Authority (OJK), the study identifies critical areas of convergence and divergence. Findings indicate that DAOs demonstrate strong transparency due to on-chain records, but remain weak in accountability, stakeholder alignment, risk management, and compliance. These weaknesses generate friction with Indonesia’s fragmented regulatory landscape, where crypto assets are classified as commodities, prohibited as payment instruments, and inconsistently taxed. Stakeholder management emerges as a pressing concern, as token-weighted voting privileges large holders while Indonesian retail investors face governance literacy gaps. To address these challenges, the paper proposes a hybrid governance framework that integrates IT governance principles with DAO structures, supplemented by compliance mechanisms tailored to Indonesian regulations. Recommendations include the introduction of accountability representatives, adoption of quadratic voting, adaptation of COBIT-based risk management, and establishment of regulatory sandboxes. The study contributes to the discourse on DeFi governance by offering a conceptual model for reconciling decentralized innovation with national regulatory frameworks in emerging markets such as Indonesia.
We present BATTLE for Bitcoin, a DoS-resilient dispute layer that secures optimistic bridges between Bitcoin and rollups or sidechains. Our design adapts the BATTLE tournament protocol to Bitcoin's UTXO model using BitVM-style FLEX components and garbled circuits with on-demand L1 security bonds. Disputes are resolved in logarithmic rounds while recycling rewards, keeping the honest asserter's minimum initial capital constant even under many permissionless challengers. The construction is fully contestable (challengers can supply higher-work counter-proofs) and relies only on standard timelocks and pre-signed transaction DAGs, without new opcodes. For $N$ operators, the protocol requires $O(N^2)$ pre-signed transactions, signatures, and message exchanges, yet remains practical at $N\!\gtrsim\!10^3$, enabling high decentralization.
Financial fraud has been growing exponentially in recent years. The rise of cryptocurrencies as an investment asset has simultaneously seen a parallel growth in cryptocurrency scams. To detect possible cryptocurrency fraud, and in particular market manipulation, previous research focused on the detection of changes in the network of trades; however, market manipulators are now trading across multiple cryptocurrency platforms, making their detection more difficult. Hence, it is important to consider the identification of changes across several trading networks or a `network of networks' over time. To this end, in this article, we propose a new change-point detection method in the network structure of tensor-variate data. This new method, labeled TenSeg, first employs a tensor decomposition, and second detects multiple change-points in the second-order (cross-covariance or network) structure of the decomposed data. It allows for change-point detection in the presence of frequent changes of possibly small magnitudes and is computationally fast. We apply our method to several simulated datasets and to a cryptocurrency dataset, which consists of network tensor-variate data from the Ethereum blockchain. We demonstrate that our approach substantially outperforms other state-of-the-art change-point techniques, and the detected change-points in the Ethereum data set coincide with changes across several trading networks or a `network of networks' over time. Finally, all the relevant \textsf{R} code implementing the method in the article are available on https://github.com/Anastasiou-Andreas/TenSeg.
The programmable and composable nature of smart contract protocols has enabled the emergence of novel market structures and asset classes that are architecturally frictional to implement in traditional financial paradigms. This fluidity has produced an understudied class of market dynamics, particularly in coupled markets where one market serves as an oracle for the other. In such market structures, purchases or liquidations through the intermediate asset create coupled price action between the intermediate and final assets; leading to basket inflation or deflation when denominated in the riskless asset. This paper examines the microstructure of this inflationary dynamic given two constant function market makers (CFMMs) as the intermediate market structures; attempting to quantify their contributions to the former relative to familiar pool metrics such as price drift, trade size, and market depth. Further, a concrete case study is developed, where both markets are constant product markets. The intention is to shed light on the market design process within such coupled environments.
Transaction Fee Mechanisms (TFMs) study auction design in the Blockchain context, and emphasize robustness against miner and user collusion, moreso than traditional auction theory. \cite{chung2023foundations} introduce the notion of a mechanism being $c$-Side-Contract-Proof ($c$-SCP), i.e., robust to a collusion of the miner and $c$ users. Later work \cite{chung2024collusion,welfareIncreasingCollusion} shows a gap between the $1$-SCP and $2$-SCP classes. We show that the class of $2$-SCP mechanisms equals that of any $c$-SCP with $c\geq 2$, under a relatively minor assumption of consistent tie-breaking. In essence, this implies that any mechanism vulnerable to collusion, is also vulnerable to a small collusion.
Abstract Stronger health systems are better equipped to withstand shocks and continue providing quality services as response measures are implemented. We conducted a systematic review to synthesize the understanding of the concept of health system resilience from various stakeholders in Africa, focusing on definitions and attributes of a resilient health system. We conducted a search for peer-reviewed articles and grey literature, filtered for Africa, from 1980 to 2023, using the SPIDER framework. We searched four databases: PubMed, the Bielefeld Academic Search Engine, the Cumulative Index to Nursing and Allied Health Literature, and Scopus, and reviewed the websites of the World Health Organization, Africa CDC, and Ministries of Health of African countries. Articles were selected based on set inclusion and exclusion criteria. Qualitative articles were appraised using the Critical Appraisal Skills Programme, and mixed-methods articles using the Mixed Methods Appraisal Tool. We mapped the distribution of included articles by country studied; categorized the articles based on reported shock, health system building block described; and identified the definition of health system resilience, and its attributes in each article. The search yielded 4,306 relevant records, fifty-five of which were included in the study. Studies were found from 48 of the 54 African countries. Up to 75% of the articles focused on COVID-19; others were on Ebola Virus Disease, cholera, and meningitis. Service delivery and health workforce were the most frequently studied health system building blocks. In defining or describing health system resilience, the adaptive capacity (39, 65%) was most frequently mentioned, followed by absorptive capacity (17, 28%), preparedness (3, 5%), and recovery (1, 2%). Identified attributes of a resilient health system were: community engagement and involvement; leadership and governance; collaborations and partnerships; human resources for health; health education and promotion; health information systems; health service delivery; decentralization and local governance; health infrastructure and logistics; preparedness; learning and adaptation; and innovation and financing. Our review reports four core capacities that define a resilient health system: preparedness, absorptive capacity, adaptive capacity, and recovery. Essential attributes encompass community engagement, health education and promotion, leadership and governance, surveillance and laboratory capacity, innovation, service delivery, and adaptability.
Mohamed Amine Nabli, Ikrame Ben Slimane, Haykel Hamdi
Purpose This study explores the quantile-on-quantile connectedness between major European listed football clubs and Bitcoin, providing a deeper understanding of their interdependencies. The selection of these assets is motivated by their prominent roles in both financial and sports markets, especially during periods of market volatility. By employing advanced portfolio optimization strategies, the research examines how these strategies enhance resilience and effectively manage risk during periods of market volatility. Design/methodology/approach Utilizing the quantile-on-quantile connectedness framework by Gabauer and Stenfors (2024), a robustness test is conducted using Quantile Granger Causality analysis by Jeong et al. (2012). Optimal investment portfolios are constructed using three strategies: Minimum Variance Portfolio (MVP), Minimum Correlation Portfolio (MCP) and Minimum Connectedness Portfolio (MCoP). The research analyzes a decade of data (2014–2024) from major European listed football clubs. Findings Results demonstrate that inversely related quantiles exhibit stronger total connectedness than directly related ones, highlighting the importance of managing tail risks. Bitcoin displays characteristics of a safe-haven asset during market downturns, yet under specific conditions, it can act as a shock transmitter for clubs such as Juventus and Olympique Lyonnais. Portfolio analysis indicates that Bitcoin serves as a critical diversification tool, with its optimal allocation varying across different strategies. Research limitations/implications These findings provide important insights into the dynamic relationship between football clubs and Bitcoin, offering practical implications for investors and portfolio managers. This study’s focus on market volatility and tail risks highlights Bitcoin’s role in improving portfolio resilience, enabling more informed decision-making in investment strategies. Originality/value This study contributes to the existing literature by exploring the novel interplay between European football clubs and Bitcoin using quantile-based connectedness analysis. It underscores the strategic role of Bitcoin as a diversification tool, offering valuable insights into risk management and portfolio optimization in dynamic financial markets.
Smart contracts are central to blockchain ecosystems, yet their development remains technically demanding, error-prone, and tied to platform-specific programming languages. This paper introduces SCEditor-Web, a web-based modeling environment that combines model-driven engineering (MDE) with generative artificial intelligence (Gen-AI) to simplify contract design and code generation. Developers specify the structural and behavioral aspects of smart contracts through a domain-specific visual language grounded in a formal metamodel. The resulting contract model is exported as structured JSON and transformed into executable, platform-specific code using large language models (LLMs) guided by a tailored prompt engineering process. A prototype implementation was evaluated on Solidity contracts as a proof of concept, using representative use cases. Experiments with state-of-the-art LLMs assessed the generated contracts for compilability, semantic alignment with the contract model, and overall code quality. Results indicate that the visual-to-code workflow reduces manual effort, mitigates common programming errors, and supports developers with varying levels of expertise. The contributions include an abstract smart contract metamodel, a structured prompt generation pipeline, and a web-based platform that bridges high-level modeling with practical multi-language code synthesis. Together, these elements advance the integration of MDE and LLMs, demonstrating a step toward more accessible and reliable smart contract engineering.
With the growth of digital financial systems, robust security and privacy have become a concern for financial institutions. Even though traditional machine learning models have shown to be effective in fraud detections, they often compromise user data by requiring centralized access to sensitive information. In IoT-enabled financial endpoints such as ATMs and POS Systems that regularly produce sensitive data that is sent over the network. Federated Learning (FL) offers a privacy-preserving, decentralized model training across institutions without sharing raw data. FL enables cross-silo collaboration among banks while also using cross-device learning on IoT endpoints. This survey explores the role of FL in enhancing financial security and introduces a novel classification of its applications based on regulatory and compliance exposure levels— ranging from low-exposure tasks such as collaborative portfolio optimization [16] to high-exposure tasks like real-time fraud detection [7], [8]. Unlike prior surveys, this work reviews FL’s practical use within financial systems, discussing its regulatory compliance and recent successes in fraud prevention and blockchainintegrated frameworks. However, FL’s deployment in finance is not without challenges. Data heterogeneity, adversarial attacks, and regulatory compliance make implementation far from easy. This survey reviews current defense mechanisms and discusses future directions, including blockchain integration, differential privacy, secure multi-party computation, and quantum-secure frameworks. Ultimately, this work aims to be a resource for researchers exploring FL’s potential to advance secure, privacycompliant financial systems.
Decentralized coordination and digital contracting are becoming critical in complex industrial ecosystems, yet existing approaches often rely on ad hoc heuristics or purely technical blockchain implementations without a rigorous economic foundation. This study develops a mechanism design framework for smart contract-based resource allocation that explicitly embeds efficiency and fairness in decentralized coordination. We establish the existence and uniqueness of contract equilibria, extending classical results in mechanism design, and introduce a decentralized price adjustment algorithm with provable convergence guarantees that can be implemented in real time. To evaluate performance, we combine extensive synthetic benchmarks with a proof-of-concept real-world dataset (MovieLens). The synthetic tests probe robustness under fee volatility, participation shocks, and dynamic demand, while the MovieLens case study illustrates how the mechanism can balance efficiency and fairness in realistic allocation environments. Results demonstrate that the proposed mechanism achieves substantial improvements in both efficiency and equity while remaining resilient to abrupt perturbations, confirming its stability beyond steady state analysis. The findings highlight broad managerial and policy relevance for supply chains, logistics, energy markets, healthcare resource allocation, and public infrastructure, where transparent and auditable coordination is increasingly critical. By combining theoretical rigor with empirical validation, the study shows how digital contracts can serve not only as technical artifacts but also as institutional instruments for transparency, accountability, and resilience in high-stakes resource allocation.