BITCOIN AS A COMPONENT OF THE MONETARY SYSTEM: SOCIAL INTEGRATION, TECHNOLOGICAL DIFFUSION AND SOCIO-POLITICAL TRANSFORMATIONSThe aim of this article is a multifaceted analysis of Bitcoin as a component of the contemporary monetary system, identifying the processes of its socialization, technological diffusion, and the consequences of changes in the socio-political space. The author successively presents indicators of Bitcoin’s social and economic integration and the development of blockchain as a tool transforming trust, legitimization, and the structure of value circulation. Particular emphasis is placed on analyzing the process of blockchain technology diffusion and the social mechanisms of legitimizing new forms of value. The article concludes with the thesis that Bitcoin, transcending its financial function, has become a component of a broader civilizational shift – a harbinger of the transformation of the model of money, ownership, and sovereignty in the era of Web 3.0.
The Costello Constant (CC) Formula base (e/phi - 1/pi) and the Recursive Costello sequence it was extracted from that's governed by the Rule n(+1) = n + f(n), where f(n) is the Greatest Proper Divisor of n(-1); f(n1) = 1. Which locks into an OOE or OE cycle, When mapped onto the complex plan Y(ix) = (e/phi -1/pi)^(0±ix) and use x as a function of time to cretes a 3rd dimention frma a duel helix where intersection of the 2 spiraling lines cancel out from complete annihilation and return a value of zero when calculated, this helix is anchored to the origin by raising it to the power of zero, the even exponent of I is one helical arm, the negative value of I is the odd value helical arm. Points where they annihilate the x values are the zeta zeros value with a frequeny ~ 10.33715124… the slope of the sequence points on a semi logarithmic graph when they align perfectly straight… or the inverse of... when joining sequential odds treating the O O E cycles as only 2 values (plot points, both odds as one single unit, multiplied by the value of CC ~ 1.3616... gives the exact value zeta zero 1, in the sequence this is equivalent to the Attractor a10 (16) when looking at ratios between zero 1 and zero 2 as an x/y it matches exactly to (13+16+17/3)/(17/25/26) this number and it's simplest reduced form 268/183 also are the exact ratio of certain toma in chemicals. And te genes which map a certain protein. I assume other ratios between consecutive numbers and the sequence will reveal some wonders in the universe that have remained untold until this moment. I've been ignored for weeks now which has giving me the time to dive into a level of certainty beyond any shadow of a doubt. On the regular graph when treating odds consecutive as one and evens as one connecting all evens and connecting All Odds creates two distinct lines where are the formula of the Costello constant is right in the middle. Basically turning the Zeta zeros into an algebraic problem by connecting the dots odds and evens where intersects on the equation graphed is the location of the Zeta zeros. Mic drop. V6. Added details about the zero timing overlap with formula being dictated by timing of pair sequential numbers in the sequence being used. V7. Added Defining Costello Constant's Value, Definition, And Symbol. V8. Added Data Set Of Sequence Numbers As T Values V9. Eureka! Offset fixed! "^0 + it" is the golden key it's officially solved. The Costello spiral is the structure, The zeta zeros are mapping the features of it. V10. Added Needed Proof V11. Complete revamp fixing errors in construction. I'm a non-academic... I'm trying here... Alone... V12. Updated Formatting Pages 1 - 2 Finalized V13. Update Pages 1 - 3 Finalized, 4 - 7 Drafted V14. Finalized Doc 1 Current Version Is A Fully Closed Loop System Logic, It's Proof By Fundamental Law. Costello Spiral Diagrams Reflects Older .809... Helix Radius Matching Pre 1.0000 Radius Formula Reduction. "This Fundamental Law is scale-invariant; while earlier diagrams (0.809) and the finalized 1.0000 reduction represent different magnitudes, the underlying closed-loop logic and intersection intersections remain constant. The 1.0000 Unit Radius represents the simplest, normalized state of the Costello Spiral." One last note to whom it may concern... I did this completely independent starting from the ground up with no previous research into other publishments, I started with the desire to make a sequence that was novel, and just kept making connections one after another. I've watched a couple YouTubes in the past that had discussed vaguely The mystery of the Zeta zeros and that's about the extent of my outside knowledge. I didn't set out to discover the secret for it, my series ran into it by its nature itself. V15. Updated format to Latex, added much more vigorous math proof, order of logic still needs tweaking. V16. Added data point charts into Latex pdf. V17. Formatting Fixes V18. Added -1 somewhere... Oops V19. Added how the Costello Spiral solves the Collatz Conjecture too. V20. Added hypothesis of the twin Prime conjecture V21. Fixed Rooke Mistakes... Double Statements... Out of order stuffs.... V22. More Formatting Fixes. V23. Lots better, 25+ years sine education environment, first proof... Getting there... V24. Added formula for ratio relationship of factors to the zero spacing, but messes up my formatt big time... Lullz.. im fixing it. I hate all these loops I have to jump through honestly, taking away from time that I could just be diving further in the numbers as usual. I'm almost giving up a couple times I just went back to my paper notebooks. V25. Well maybe have about 10% of the information out now... Main problem is I don't know what's most important to show I don't know what the world knows or not... Like I don't know what to add next the list is too big... Semi-prime Costello sequence numbers that are close together align with Zeta zeros close together.. eg., 7171... So much work... I've tried showing my math and I get laughed at... I'mma just keep on pushing... It may not be conventional to add your thoughts or whatever... But I'm a break the fifth wall right now... From two weeks now I've tried reaching out... All skepticism.. it just hit me tonight... It's because it's all sounds too good to be true... I didn't know that... I'm trying to do too much at once... I mean on top of my work that I'm doing I had to learn the formal language... I've had to learn how to code... I've had to learn Python script so I can run my old numbers... And for 2 weeks now I've been pushing... To show people ONE of my creations. Maybe the world is just not ready.... .. .. . Maybe. It's hard to forget, everything I regret. So why do I neglect, the chances that I get, To make those things correct... When I've tried to reflect... I just lost more respect... How did i ever let my mindset behind set get so inept. While im On the subject if I may be direct. I digress... It is best to get the rest of my chest. Im blessed but made a mess whats more or less my nest. I feel i failed my quest, I have failed my own test. It's a sure bet soon I'll take my last breath. Back to work... V26. Gtting there... Please use V23 complete copy until i stop mesing up my work with copy pasts twice deleed everything. V Edition2 V27. New formatt next few additions should be coming back to back to back as I string the old with the new. Refer to V22/23 for older complete outline, V Edition2 V28. Brought over some data from my research pfd, order and simplification are needed. V Edition2 V29. Stitching in the dimensional transitions from the number line to a real plane to complex plane to the manifold. Still need smooth transitioning. V Ediion2 V30. Added a good chunk to complex/manifold section, I just want to get it uploaded, I still have to prune it and smooth it. And make sure the stuff at the end is stated the way it's supposed to before I can remove it. Editiom2 V31. Added 10.3 frequency of spiral is the slope of sequence on log xy. Deleted doubles. Edition2 V32 Added dada set at end, refining python code number generator to add next. Edition2 v33 Changed Description on Zenodo added some info to I - III, refer to Ver 23 in tandem as f now after reading to complete the info aquired. Lots more to come... Edition2 v33.2 Keep Pushing Unil The World Listens... Changed Sequence Formula Formatt of f(n) Fixed Order still have to move over more sections from research Pdf. Including making sure pdf reflects duel helix is intersecting as counter clockwise 1 string and clockwise the other, reforming old 180° opposition, to actual intersection. At 0° Edition2 v34. Updated High Precision Value Of Slope using 500 sequence Values, Added bar graph for delta 2 equalization, other minor adjustments. Edition2 v35. Fixing all formulas to compensate for the change of what f(a_n) is.. as befor the rule a_n+1 = a_n + f(a_n-1) when f(a_n) meant a_n's GPD.. but for clearity f(a_n) now means a_n-1's GDP... To remove a LAG extra thought... Royal pain but a necessity.... Almost done converting everything. Edition2 v36 Formalized Pages 1-2 of actual proof after index, added rigor and made it more succinct. Eution2 v37. Showed how 10.337... slight miss alignment snap perfectly to 10.333 and perfectly aligned to zz1 now that start up terms 1-9 are removed from calculations. Edition2 v38 Formed formulas using the costello constant for prime density and how many primes exist in any limit, gives exct answer at 1,000,000. Christopher Michael Costello SomeDumbTrucker@gmail.com
This article reviews how blockchain-native finance is reshaping financial intermediation and how the next wave of digital finance is likely to be influenced by large language models (LLMs) and quantum finance research. Building on recent work on decentralized finance, blockchain implementation, supply chain finance, and emerging FinTech architectures, the study develops an integrated analytical framework that connects three layers of change: programmable settlement, intelligent decision support, and frontier computational finance. Rather than treating DeFi, blockchain-based supply chain finance, LLM applications, and quantum finance as isolated topics, the review shows that they form a continuous innovation trajectory with shared challenges in governance, interoperability, data quality, risk modeling, and institutional trust. The paper synthesizes prior findings, compares major technical and managerial mechanisms, and proposes a research agenda for resilient, explainable, and regulation-aware financial innovation. The results suggest that blockchain creates a credible record and execution layer, LLMs expand interpretive and operational intelligence, and quantum finance may eventually widen the solution space for complex risk-pricing and portfolio problems. The article concludes with practical implications for platform designers, regulators, and industry managers.
Abstract This research presents a regime-aware hybrid forecasting framework for the Bitcoin market’s nonlinear, nonstationary and regime-switching behavior. The architecture integrates econometric models, neural forecasting and meta-learning, unified under a regime-detection mechanism using probabilistic inference. Central to the approach is a Hidden Markov Model (HMM) trained on log returns, which infers latent market regimes, bull, bear and sideways, based on statistical characteristics rather than arbitrary thresholds. Each detected regime triggers a specialized forecasting model: ARIMAX for volatile bear markets, SARIMAX for cyclical sideways periods and NeuralProphet for nonlinear bullish dynamics. These models leverage historical returns (Jan. 2012-Jun. 2025) and external signals, including technical indicators (RSI, MACD, Bollinger bands) and volatility metrics. A meta-learning layer, implemented via XGBoost, dynamically selects the optimal model at each time step based on the regime. This enables real-time adaptation to evolving market conditions. Predictions are made on log returns and translated into price forecasts through exponentiation. The framework’s performance is evaluated using R 2 , Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The regime-aware model outperforms the no-regime model significantly across all metrics, especially in error reduction (MAE cut by ~ 56%) and higher explanatory power (R² increased from 0.82 to 0.91). Ablation results confirm the structural validity of the proposed framework, with the regime–model assignment (ARIMAX for bear, SARIMAX for sideways, NeuralProphet for bull) achieving the lowest forecasting error (MAE = 736, R 2 = 0.93) at the yearly level and outperforming alternative configurations. The inferred regimes exhibit economically meaningful persistence (average durations 14.8–22.4 days) and transition stability (diagonal probabilities 0.91–0.94). The meta-learning component shows coherent and interpretable behavior, with regime labels and recent model errors explaining nearly 70% of decision weight and regime-consistent model selection exceeding 80%. These forecasting gains translate into tangible economic benefits: in a six-month backtest, the proposed strategy delivers the highest return (19%), lowest drawdown (19%) and highest Sharpe ratio (1.01), outperforming all benchmarks.
This study focuses on preventing unauthorized recipient transactions within the Ethereum blockchain system. Unauthorized recipient transactions occur when a sender transfers cryptocurrency without the recipient's awareness, posing risks such as the recipient being implicated in crimes such as suspected involvement in money laundering. Previous research has designed a transaction restriction function using smart contracts tailored to Ethereum's unique blockchain model. This prevention mechanism was implemented on open-source software and its functionality verified. This study proposes a method to enhance the security of processing conducted to investigate the relationship between senders and receivers. We implement this method in open-source software and demonstrate its effectiveness.
Reentrancy remains one of the most critical classes of vulnerabilities in Ethereum smart contracts, yet widely used detection tools and datasets continue to reflect outdated patterns and obsolete Solidity versions. This paper adopts a dependability-oriented perspective on reentrancy detection in Solidity 0.8+, assessing how reliably state-of-the-art static analyzers and AI-based techniques operate on modern code by putting them to the test on two fronts. We construct two manually verified benchmarks: an Aggregated Benchmark of 432 real-world contracts, consolidated and relabeled from prior datasets, and a Reentrancy Scenarios Dataset (RSD) of \chadded{143} handcrafted minimal working examples designed to isolate and stress-test individual reentrancy patterns. We then evaluate 12 formal-methods-based tools, 10 machine-learning models, and 9 large language models. On the Aggregated Benchmark, traditional tools and ML models achieve up to 0.87 F1, while the best LLMs reach 0.96 in a zero-shot setting. On the RSD, most tools fail on multiple scenarios, the top performer achieving an F1 of 0.76, whereas the strongest model attains 0.82. Overall, our results indicate that leading LLMs outperform the majority of existing detectors, highlighting concerning gaps in the robustness and maintainability of current reentrancy-analysis tools.
Blockchain technology introduces asset types and custody mechanisms that fundamentally break traditional financial auditing paradigms. This paper presents an autoethnographic analysis of cryptoasset auditing challenges, build on top of prior research on a comprehensive framework addressing existence, ownership, valuation, and internal control verification. Drawing from lived experience implementing blockchain systems as an engineer, smart contract auditor, and CTO of a publicly traded cryptoasset firm, we demonstrate how autoethnographic methodology becomes necessary for understanding technical complexities that external analysis cannot capture. Through detailed examination of token airdrops, multi-signature smart contracts, and real-time on-chain reporting, we provide experimental approaches and common scenarios that auditing firms can analyze to address blockchain innovations currently considered technically insurmountable.
André Romão, Francisco Faria, João R. Matos, Emanuel Nunes · 6 authors
Enterprise adoption of permissioned blockchains remains limited due to the complexity and cost of integrating legacy systems. We present a modular adapter architecture that bridges enterprise applications with blockchain networks, designed to support small and medium-sized enterprises with limited technical resources. The architecture provides five key modules: (1) configurable data extractors supporting diverse interfaces such as APIs and file uploads, (2) data transformers that can convert to standard formats, (3) messaging middleware to ensure operations can tolerate lack of connectivity and traffic spikes, (4) blockchain loader to commit transactions to the blockchain, and (5) status visibility to collect and expose runtime metrics that support operational transparency. We validated the adapters through a pilot deployment in a real-world fruit supply chain, involving three distinct enterprises. The pilot achieved blockchain integration with minimal workflow disruption, demonstrating the usefulness of these adapters for practical interoperability of existing systems with the blockchain.
Transfer-based anti-money laundering (AML) systems monitor token flows through transaction-graph abstractions, implicitly assuming that economically meaningful value migration is sufficiently encoded in transfer-layer connectivity. In this paper, we demonstrate that this assumption, the bedrock of current industrial forensics, fundamentally collapses in composable smart-contract ecosystems. We formalize two structural mechanisms that undermine the completeness of transfer-layer attribution. First, we introduce Principal-Execution-Beneficiary (PEB) separation, where intent originators, transaction executors (e.g., MEV searchers), and ultimate beneficiaries are functionally decoupled. Second, we formalize state-mediated value migration, where economic coupling is enforced through invariant-driven contract state transitions (e.g., AMM reserve rebalancing) rather than explicit transfer continuity. Through a real-world case study of role-separated limit order execution and a constructive cross-pool arbitrage model, we prove that these mechanisms render transfer-layer observation neither attribution-complete nor causally closed. We further argue that simply expanding transfer-layer tracing capabilities fails to resolve the underlying attribution ambiguity inherent in structurally decoupled execution. Under modular composition and open participation markets, these mechanisms are structurally generative, implying that heuristic-based flow tracing has reached a formal observational boundary. We advocate for a paradigm shift toward AML based on execution semantics, focusing on the restitution of economic causality from atomic execution logic and state invariants rather than static graph connectivity.
In the context of the economy digitalization and the information technologies’ active development, cryptocurrency fraud poses an increased social danger and is characterized by a high level of latency, a transnational nature, and difficulties in detection. Purpose: to determine the content and structure of the cryptocurrency fraud’s forensic characteristics of and to identify forensically significant features relevant to the initial stage of investigation. Methods: general scientific methods of analysis and synthesis, induction and deduction, as well as special forensic methods, including the systems-and-activity approach, formal logical analysis, forensic modeling, and the generalization of investigative and judicial practice. Results: it is substantiated that the forensic characteristics of cryptocurrency fraud have independent practical significance and function as an information-oriented category. Its main elements are highlighted, the specificity of the digital trace pattern is revealed, and the role of digital traces as a primary source of evidential information is also shown. The study concludes that the use of forensic characteristics is advisable when formulating investigative hypotheses, planning investigations, and selecting tactical techniques.
This paper establishes the Equality of Wealth Creation principle within the Natural Economic Wealth (NEW) framework: any algorithmic execution satisfying Axioms 1, 2, and 3 of Paper 0 constitutes wealth creation and is recorded in the distributed ledger with full Qoin attribution, regardless of whether it is recognised, monetised, or valued by any existing economic system. The restriction that orthodox economics imposes requiring financial mediation as a precondition for economic recognition has no physical basis. It is an institutional convention, and Axiom 1 dissolves it by measuring what physically occurs rather than what the financial system records.
This paper derives, from first principles, the architecture of the distributed public ledger that forms the operational core of the Natural Economic Wealth (NEW) framework. The ledger is not a financial instrument, a blockchain token system, or an accounting convention. It is the informational substrate through which the direct attribution of algorithmic execution to its directing intelligence is operationally realised. The physical justication for the ledger's immutability is the Second Law: algorithmic executions are thermodynamically irreversible, and their record must be equally so. The ledger closes the cybernetic loop between measurement and agent, records wealth creation and consumtion events, enables a wealth profiling system, and constitutes the civilisational memory of all productive algorithmic execution within the framework. Community-governed federated architecture prevents centralisation and institutional capture. The Qoin unit is introduced and dened as the physical unit of account for ledger records. The marketplace description record the mechanism by which Qoin production gures are attached to ordered states entering the marketplace is introduced as a ledger-adjacent informational structure that enables the consumer selection pressure
This paper systematically reviews the research foundation, core technologies, and practical applications of cryptography in the blockchain field. Algorithms, and data immutability relies on cryptographic hash functions and Merkle tree structure; the balance between transparency and privacy in block chain relies on the encryption technique of zero-knowledge proofs, ring signature, homomorphic encryption. Therefore, every part of block chain is based on cryptography; without the mathematical guarantee of cryptography, the trust decentralized by block chain is meaningless. The security of block chain mainly relies on the encryption techniques such as hash functions, digital signatures and encryption algorithms, and traditional cryptographic methods will have vulnerabilities when facing quantum computing, because quantum computer may be used to break currently commonly used algorithms such as RSA, ECC eventually. This “security paradox" requires us to pay more attention to block chain technologies, because block chain technology needs to advance in tandem with cryptography. Traditional blockchain technologies can’t be used indefinitely. Against this background, researching block chain －based crypto is of great theoretical significance and practical value: on the one hand, researching on new cryptographic methods applicable to block chain can extend the area of cryptosystems and give people a new way of solving the security problems in block chain; on the other hand, we should not neglect the possibility of breaking the block chain by combining quantum computing with cryptanalysis research.
State payment systems today play a central role in accelerating economic transactions, ensuring transparency in budget fund movements, and digitizing financial services provided to citizens. From this perspective, DeFi – decentralized finance—emerged as a new architecture compared to traditional banking infrastructure and belongs to the category of technological solutions applicable in state payment systems. The core idea of DeFi is to replace intermediaries with code, automate transactions through smart contracts, and operate on open blockchain infrastructure.[1]..
Federated graphs learning for graphs enables multiple clients to share model knowledge and engage in collaborative training while ensuring user data privacy. Nevertheless, federated learning for graphs also faces various security threats, such as privacy leakage and malicious attacks. On the other hand, compared with other security strategies, differential privacy offers low cost and high efficiency in protecting data in federated learning, yet it can compromise the training accuracy of federated learning for graphs and, in some cases, severely degrade training performance. Therefore, this paper considers noise-sensitive scenarios where even a small amount of noise can significantly impact training, and integrates knowledge distillation with distributed differential privacy federated learning for graphs. This approach enhances model training accuracy under noise-sensitive conditions while mitigating the adverse effects of differential privacy noise on training, all while ensuring model security. In addition to leveraging differential privacy to protect data and parameter privacy, we further aim to defend against malicious client attacks. By establishing a global consensus on the gradient clipping range, we use zero-knowledge proofs to provide sampled verification of the gradient range, demonstrating that the parameters uploaded by clients have been correctly clipped during training. Parameters that fail the verification are discarded, thereby further enhancing security.
Privacy-Preserving Technologies in Data
Advanced Graph Neural Networks
Distributed Sensor Networks and Detection Algorithms
Donations constitute a critical component of social welfare. Most traditional donation systems are built on centralized architectures, making them prone to single points of failure and lacking transparency. Blockchain-based donation systems either sacrifice privacy to ensure auditability or make auditing cumbersome in order to protect privacy. In this paper, We propose a donation system that integrates SGX with Hyperledger Fabric (Fabric for short). First, we ensure reliable authentication of donors and donees through the identity verification mechanism of Fabric. Second, we leverage SGX hardware protection technology to provide privacy protection for sensitive chaincodes. Third, we designed an audit algorithm based on non-interactive zero-knowledge proofs, enabling efficient auditing while protecting data privacy. Additionally, our deposit mechanism implemented via chaincode and physical-world evidence mechanism effectively reduce the risk of theft during material transportation. Experimental results from the prototype system indicate that the proposed scheme ensures functional integrity while achieving computational efficiency superior to comparable solutions.
Financial Fraud has become increasingly common today due to the decentralized finance systems. It involves illegal activities that take over our finances without our knowledge, potentially causing huge losses and negatively affecting economic integrity. Financial fraud erodes trust among the general public, investors, and customers, destabilizing the financial system and hindering economic development. In this Research paper, we aim to explore methods for preventing these fraudulent activities using Artificial Intelligence. It studies the methods and tools we can use to reduce financial fraud. As technology advances, we now have artificial intelligence, which enables us to use modern techniques to combat fraud. We can use various Artificial Intelligence tools like Machine Learning, Deep Learning, Natural Language Processing, Anomaly Detection, Reinforcement Learning, Graph method, and various other tools to recognize the unidentified patterns in our financial transactions and save ourselves from financial fraud. Furthermore, it is essential to implement robust security systems within decentralized finance platforms. This study on enhancing security systems and preventing financial fraud will be helpful to future developers, Researchers, Investors, Individuals, Regulatory bodies, and Security Firms. The goal is to make decentralized finance systems more secure to mitigate the risk of financial fraud and to protect the economic integrity for sustained economic development. Based on this study we will able to upgrade the security system of our financial transactions by using various artificial intelligence tools and can reduce the number of frauds. While completely eliminating financial fraud is challenging, we can significantly reduce it through concerted efforts, creating awareness, utilizing artificial intelligence tools, and exercising vigilance.
Blockchain technology and smart contracts are revolutionizing legal and commercial transactions worldwide. These innovations enhance efficiency, automation, and security in contract execution while reducing reliance on intermediaries. However, their adoption presents legal challenges related to enforceability, regulatory oversight, and dispute resolution. This research examines the UAE's legal framework governing blockchain and smart contracts, analysing their recognition under contract and commercial law, as well as the roles of key regulatory UAE authorities, including the Securities and Commodities Authority (“SCA”) and the Virtual Assets Regulatory Authority (“VARA”).
The proliferation of sophisticated AI and bot networks necessitates robust methods for verifying human uniqueness and liveness in digital ecosystems. Existing Proof-of-Humanity (PoH) solutions rely on centralized authorities, invasive static biometrics, or socially-correlatable data, creating vulnerabilities in privacy, security, and accessibility. We introduce the IAM Protocol, a decentralized framework for PoH and Self-Sovereign Identity built on Solana. The core innovation is temporal consistency: the assertion that human identity is best proven not by a static secret, but by the bounded, chaotic drift of biological and behavioral patterns over time. The framework captures multi-modal behavioral data (voice prosody, hand tremor, touch dynamics) during a configurable behavioral challenge, extracts a 134-dimensional feature vector, and produces a 256-bit locality-sensitive hash via SimHash. A Groth16 zero-knowledge proof verifies that consecutive fingerprints fall within a bounded Hamming distance without revealing either value. Attestations are anchored to non-transferable identity tokens (SPL Token-2022) with progressive Trust Scores. We provide formal security definitions, analyze the protocol against replay, synthesis, and Sybil attacks, introduce a graduated trust model distinguishing first-time liveness checks from sustained temporal consistency, and present benchmarks from a working implementation deployed on Solana devnet.
Traditional Byzantine Fault Tolerance (BFT) consensus algorithms effectively tolerate node behavioral faults but lack the ability to verify the quality of input data. This makes them vulnerable to security risks from low-quality or “compliant yet malicious” data in data-driven applications. To address this gap, we propose a Data-Quality-Driven Byzantine Fault Tolerance algorithm based on Zero-Knowledge Proofs, called Q-BFT. The algorithm introduces a “quality gate” prior to classic BFT consensus—an on-chain verification phase that uses zk-SNARKs and is automated by smart contracts. This allows nodes to prove in zero-knowledge that their data meets predefined thresholds for accuracy, completeness, and consistency without exposing raw data. Passing the verification becomes a prerequisite for joining consensus voting. We design a two-layer smart contract architecture that efficiently orchestrates off-chain proof generation and on-chain automated verification. Experiments show that in a 100-node network with 30% malicious nodes, Q-BFT improves the consensus success rate from 41.5% (with PBFT) to 96.4%, while maintaining federated learning global model accuracy above 88%, in contrast to the model collapse (< 20% accuracy) observed under a traditional BFT protocol. The system achieves an average verification latency below 0.65 s and a throughput of 735 TPS(Transactions Per Second), striking an effective balance among security, privacy preservation, and operational efficiency. By enforcing privacy-preserving data quality verification as a mandatory gate before consensus, Q-BFT thus provides a high-assurance foundation for data-sensitive and privacy-critical applications. It addresses the core vulnerability of traditional consensus in scenarios like federated learning, where model integrity depends on participant data quality, and trustworthy data markets, where transaction validity requires assured data authenticity without exposing the data itself.
Federated learning represents a paradigm shift in distributed machine learning by enabling collaborative model training across decentralized nodes while maintaining data privacy at source locations. It helps bridge the gap between artificial intelligence-driven development guidelines and the regulatory mandates laid down by data protection legislation. A decentralized architecture transmits only the model updates to aggregation servers; this reduces privacy breach exposure and compliance violation risks and also eliminates raw data centralization. Federated learning helps build production-ready systems across healthcare, finance, and edge computing environments, owing to the maturities that have occurred in cloud infrastructure. This is a transition from the erstwhile theoretical frameworks it used to have. Architectural advantages are supplemented by privacy-preserving mechanisms like differential privacy and secure aggregation protocols, which facilitate organizations to leverage collective intelligence without exposing sensitive information. Robust platforms for privacy-critical applications can be synthesized by the integration of cloud-native security services, cryptographic enhancements, and edge computing optimization. Courtesy of emerging solutions that cater to model fairness, communication efficiency, and data heterogeneity, federated learning's practical applicability across diverse organizational contexts and regulatory domains continues to advance.
Jean-Claude Baraka Munyaka, Olivier Gallay, Edward Mutandwa, Lolemtum Joseph Timu · 8 authors
Climate change continues to undermine agricultural productivity and livelihoods in sub-Saharan Africa, where smallholder, rain-fed systems predominate. Kenya and Zimbabwe, representing contrasting decentralized and centralized adaptation systems, provide insights into how institutional design shapes agricultural resilience. This study conducts a comparative institutional analysis of climate change adaptation across four dimensions: land tenure, governance structures, access to inputs and resources, and community-based support. Using a systematic literature review (2000–2025), bibliometric mapping, and a Composite Institutional Adaptation Index (CIAI), the analysis examines how policies and local institutions interact to shape adaptive capacity. Findings indicate that Kenya's devolved governance facilitates local innovation through County Climate Change Funds, while Zimbabwe's centralized approach promotes policy coherence but constrains local autonomy. In both contexts, tenure security, equitable input access, and integration of cooperatives, traditional leaders, and women's groups emerge as critical determinants of resilience. The study situates these findings within debates on adaptation finance and governance, including Locally Led Adaptation, Green Climate Fund support, and CAADP implementation. It concludes that effective climate adaptation requires multi-scalar governance systems that integrate formal and informal institutions, align finance with local priorities, and embed learning within agricultural policy.