In the Internet of Vehicles (IoV) network, numerous vehicle terminals are required to continuously upload local data to maintain the latest service models, which supports intelligent transportation and personalized services. However, the privacy risks posed by this continuous data uploading cannot be ignored. Federated learning, as a distributed ma-chine learning paradigm, enables global model training without sharing original data. The introduction of blockchain further supports decentralization and immutability. However, federated learning also faces the risk of poisoning attacks, where malicious clients may upload abnormal or tampered model updates, severely impacting global model performance. To address this, this paper proposes a security framework that combines zero-knowledge proofs, federated learning, and blockchain. Clients use zero-knowledge proofs to ensure the legitimacy of uploaded updates, while the blockchain is responsible for verification and storage. Ultimately, a robust global model is obtained through federated aggregation. Experimental results demonstrate that this scheme effectively resists poisoning attacks, significantly improving system security and reliability while protecting user privacy.
The deployment of distributed digital twin systems in sectors such as healthcare, manufacturing, and critical infrastructure has significantly heightened the importance of data privacy. These systems interact with numerous devices and users, increasing the risk of data leakage or unauthorized access to sensitive information. Traditional centralized identity management and access control mechanisms no longer meet the scalability, autonomy, and privacy requirements of modern distributed architectures. This article explores how smart contracts operating in blockchain environments can provide decentralized access management for digital twin systems. Smart contracts enable transparent and reliable enforcement of access policies without relying on centralized authorities. The study examines the integration of modern cryptographic technologies into smart contract workflows, including zero-knowledge proofs, decentralized identifiers (DIDs), and confidential computing. These technologies make it possible to verify access rights and perform secure operations without revealing sensitive data. The article also analyzes the limitations of existing solutions, such as the high transaction costs of public blockchains, the limited performance of traditional smart contracts, and the challenges of integrating confidential computing into resource-constrained devices. The authors outline future research directions, including optimizing Layer 2 architectures to improve performance, developing secure auditing mechanisms, and ensuring compatibility with self-sovereign identity systems. The conclusions emphasize that privacy should be treated as a fundamental property of digital twin systems. In these environments, smart contracts must serve not only as governance logic but also as trusted agents that guarantee compliance with access policies and regulatory requirements in decentralized ecosystems.
The article addresses the issue of ensuringconfidential exchange of personal data in inter-organizationalinformation systems under conditions of increasing digitalinteraction between public and private sector entities. It is notedthat centralized models for processing and exchanging personaldata fail to provide an adequate level of protection againstunauthorized access, transaction tampering, and do not ensuresufficient transparency of data operations. These limitationshinder full compliance with regulatory requirements, particularlythe provisions of the General Data Protection Regulation(GDPR), ISO/IEC 27001 and 27701 standards, as well asnational legislation on information protection. The study substantiates the feasibility of using a permissioned blockchain as the architectural basis forimplementing a secure, decentralized exchange of personal datawith guaranteed access control, transaction audit, and dataimmutability. A conceptual model of the information system isproposed, involving smart contracts for managing data subjectconsent, access control, and the integration of the InterPlanetaryFile System (IPFS) for robust off-chain data storage. The modelalso includes the use of Zero-Knowledge Proof (ZKP) cryptographic mechanisms and behavioral verification criteriafor transactions. Particular attention is given to risk analysis associated withpersonal data processing in inter-organizational environments, and to the application of supplementary protection tools—suchas masking, pseudonymization, and data perturbation—tomitigate potential losses in the event of data leakage. A set oftechnical and organizational compliance criteria withinternational and national information security standards isoutlined. The aim of this research is to design an architectural modelfor inter-organizational personal data exchange based onpermissioned blockchain that ensures confidentiality, integrity, controlled access, and regulatory compliance in the field ofinformation protection.
Zero-Knowledge Proofs (ZKPs) enable users to prove knowledge of certain information without disclosing the information itself. Multi-ID ZKP systems extend this concept, allowing individuals to submit anonymous yet verifiable com plaints across various platforms while maintaining privacy and accountability. This paper explores the integration of ZKPs into complaint management systems, addressing the challenges of anonymity, verifiability, scalability, and computational efficiency. By reviewing existing literature on ZKP applications in authentication, identity management, and scalable systems, we identify key advancements and research gaps. This work aims to establish a foundation for implementing robust, privacy preserving, and efficient complaint systems leveraging multi-ID ZXP mechanisms.
This research tackles the challenges of non-independent and identically distributed (non-IID) data, socio-political inequalities in decentralized energy networks, and the unpredictable nature of renewable energy sources. It integrates blockchain technology with federated learning (FL) and game theory. Cluster-based FL paired with Shapley value allocation helps mitigate data heterogeneity, while a combined Stackelberg-Shapley model implemented via smart contracts facilitates adaptive pricing strategies. To safeguard user privacy, the system incorporates zero-knowledge proofs, differential privacy$(\varepsilon=0.5)$, and CKKS-based homomorphic encryption, achieving a 98% resistance rate against cyberattacks. Field tests in the EU's NER400 sandbox and blockchain-enabled microgrids in Kenya confirm the framework's effectiveness—achieving a 4.2% mean absolute percentage error (MAPE) in forecasting (improving from a 12% benchmark), curbing renewable energy certificate (REC) fraud by 89%, and cutting rural energy expenses by 40%. Leveraging a hybrid consensus model (PBFT with Sharding), the platform supports over 10,000 per second with sub-second latency, bridging interoperability gaps between Ethereum-based REC systems and Hyperledger platforms.
Federated Learning (FL) offers a promising paradigm for privacy-preserving collaborative training, yet it remains highly vulnerable to adversarial behaviors, client unreliability, and challenges associated with non-independent and identically distributed (non-IID) data. Existing secure aggregation techniques, while preserving confidentiality, fail to guarantee the integrity and trustworthiness of model updates, leaving FL deployments exposed to poisoning and consistency attacks. This work introduces FL-SMPC++, a robust and privacy-preserving FL framework designed to address these challenges. The primary objective is to develop a scalable solution that ensures verifiable, privacy-preserving aggregation while mitigating malicious client behaviors, dropouts, and data heterogeneity. Our approach integrates Secure Multi-Party Computation (SMPC), Pedersen commitments, and zero-knowledge proofs (ZKPs) to cryptographically bind clients' submitted updates to their validation outcomes without revealing private data. We propose a dynamic client selection strategy based on shared validation performance, a dropout-tolerant threshold aggregation protocol, and a warm-up initialization phase to counteract non-IID distributions. Comprehensive experiments on MNIST, CIFAR-10, FEMNIST, and UCI Heart Disease show that FL-SMPC++ consistently outperforms FedAvg, FedProx, and FedNova. For example, under a label-flipping attack with 30% malicious clients on CIFAR-10 (non-IID), FL-SMPC++ achieves 78.9% accuracy compared to 67.4% for FedAvg, representing an absolute gain of 11.5%. Across datasets, the framework limits accuracy degradation to 6–8% under attack, while baselines suffer 13–20% losses. These results demonstrate that FL-SMPC++ achieves strong cryptographic privacy guarantees together with empirically validated resilience and convergence, offering a scalable and practical blueprint for trustworthy FL in adversarial and resource-constrained environments. • A novel FL framework combines SMPC, commitments, and zero-knowledge proofs. • Ensures submitted model updates match validated ones without revealing them. • Uses dynamic validation for secure and fair client selection. • Tolerates client dropouts using a threshold-based aggregation mechanism. • Outperforms baseline FL methods under adversarial and non-IID conditions.
Cloud computing facilitates scalable data sharing across multiple organizations and users, but also raises concerns about data privacy. Matchmaking encryption (ME) is a prominent technique that enforces bilateral access control in cloud services such as cloud marketplace, allowing both senders and receivers to specify policies for the encrypted data to be revealed. However, receivers may be at risk of being exposed to malicious or harmful content, thus undermining their trust in cloud service platforms. To this end, we introduce MBAC, a content-moderated bilateral access control framework for privacy-preserving cloud data sharing services, which allows receivers to acquire data from authentic senders while preserving their anonymity, and report malicious content in a verifiable manner, i.e., empowering the service provider to hold senders accountable. MBAC is built upon a novel primitive called franking broadcast ME (FBME), which generates a franking signature for the data by designating the service provider as the moderator to ensure accountability and deniability, and encrypts both the data and its franking signature while embedding the sender secret key for privacy and authenticity. We then present a concrete construction of FBME from key-private public key encryption, strongly unforgeable one time signature and non-interactive zero-knowledge proof. Formal security analysis and extensive experiments demonstrate that MBAC provides efficient bilateral access control and content moderation for cloud data sharing services.
The Internet of Things (IoT) embeds various modules into physical objects to connect them to the internet. However, due to the low power and limited computing capabilities of IoT devices, it is difficult to ensure data integrity and secure access control. To address these limitations, various studies have attempted to integrate blockchain technology into IoT systems. Nevertheless, challenges such as low scalability and difficulties in storing large-scale data remain. In this paper, we propose the I2D (IOTA-IPFS-DID)-IoT system to overcome these issues. The I2DIoT system integrates Internet of Things Application (IOTA), InterPlanetary File System (IPFS), Decentralized Identity (DID), and Verifiable Credential (VC). In the proposed system, collected data is uploaded to IPFS, a distributed file system, generating a Content Identity (CID). Only the CID is recorded on IOTA, a distributed ledger technology, to ensure data integrity. In addition, a DID-based authentication mechanism enables access control without a central server. Experimental results show that the I2D-IoT system successfully performs both data integrity and access control. CPU and memory usage were measured, and CPU utilization remained under 10%, except during synchronization with the IOTA network.
Zero Trust is now the de facto standard to secure cloud-native, distributed, and AI-driven enterprise infrastructures. It's not only crucial to address human identities but also to secure non-human entities such as APIs, software agents, RPA bots, and smart city workloads. As hybrid infrastructures become the new normal and agentic AI systems (e.g., self-driving cars) grow more autonomous, identity remains the most stable and trustworthy security control plane. This document proposes an intent-aware Zero Trust Identity Architecture designed to consolidate governance, authentication, and access control for human and non-human entities. The architecture consists of decentralized identity provisioning, policy-as-code enforcement, real-time telemetry ingestion, trust scoring, and AI-powered intent detection to provide inputs for continuous verification and least privilege enforcement. Compliant with standards such as NIST SP 800-207, NIST SP 800-63, CISA Zero Trust Maturity Model, and DoD's Zero Trust Strategy, the architecture also aligns with industry developments from Microsoft Entra ID, AWS IAM Identity Center, Google BeyondCorp, SPIFFE/SPIRE, and W3C DIDs. The whitepaper explores use cases in healthcare, finance, retail, and industrial IoT spaces that are struggling with unique challenges like OT/IT convergence, multi-user devices, and governance of sensitive data access. High-profile attacks such as SolarWinds, MOVEit, and Log4Shell are broken down to highlight weaknesses in legacy IAM architectures and underscore the need for intent-based security. By intersecting behavior, purpose, and identity, this architecture remakes trust in hybrid, edge, and cloud-native settings with a conclusion of actionable paths of mitigation and a vision for intent-based Zero Trust governance
The volatile nature of cryptocurrency markets demands real-time analytical capabilities that traditional centralized computing architectures struggle to provide. This paper presents a novel hybrid cloud–edge computing framework for cryptocurrency market forecasting, leveraging distributed systems to enable low-latency prediction models. Our approach integrates machine learning algorithms across a distributed network: edge nodes perform real-time data preprocessing and feature extraction, while the cloud infrastructure handles deep learning model training and global pattern recognition. The proposed architecture uses a three-tier system comprising edge nodes for immediate data capture, fog layers for intermediate processing and local inference, and cloud servers for comprehensive model training on historical blockchain data. A federated learning mechanism allows edge nodes to contribute to a global prediction model while preserving data locality and reducing network latency. The experimental results show a 40% reduction in prediction latency compared to cloud-only solutions while maintaining comparable accuracy in forecasting Bitcoin and Ethereum price movements. The system processes over 10,000 transactions per second and delivers real-time insights with sub-second response times. Integration with blockchain ensures data integrity and provides transparent audit trails for all predictions.
This paper presents a novel Zero-Knowledge Machine Learning (zkML)-assisted framework for secure identity enrollment in Zero Trust Network (ZTN) architectures. The proposed system addresses the limitations of static credential-based authentication by integrating zkML-driven behavioral validation with permissioned blockchain-based token issuance. A Non-Fungible Token (NFT) is used to encapsulate a one-time enrollment token (OTT) encrypted with the public key of the requesting user. The zkML layer verifies behavioral features prior to token issuance, ensuring that only users with legitimate interaction patterns receive access credentials. A permissioned Ethereum blockchain handles NFT creation and ownership management, while the enrollment process is executed through OpenZiti APIs for secure overlay network participation. Experimental evaluation shows that the zkML-validated system achieves a 96.3% fake user block rate and $98.7 \%$ NFT precision, outperforming traditional methods by significantly reducing unauthorized access. Although the zkML approach introduces a modest increase in processing time, the enhanced accuracy and security justify the trade-off. This work demonstrates the potential of combining AI-driven inference and verifiable blockchain mechanisms to achieve scalable, privacypreserving, and behavior-aware enrollment in decentralized network environments.
G Anvith, Nithish Kushal Reddy, Ragini Tripathi, C. R. Kavitha
Federated learning enables multiple clients to collaboratively train a shared model without exchanging raw data, but it raises privacy and integrity concerns when model updates traverse untrusted channels. In this project, we develop a secure federated learning pipeline that combines the CKKS homomorphic-encryption scheme with Groth-16 zero-knowledge proofs to protect client updates during transmission and to verify that each update stays within an agreed-upon norm bound. We benchmark CKKS parameters (poly_modulus degree, coefficient_moduli, and scale) on real-world model vectors to identify an optimal setting—8192-degree with two primes (60-bit and 40-bit) at a 232scale—that offers sub-100 ms encryption, minimal error, and moderate ciphertext sizes ( 3.3 MB). Clients train a small convolutional network on disjoint partitions of the MSTAR SAR dataset, generate succinct ZK proofs for each 128-element weight chunk, encrypt those chunks under the selected CKKS context, and submit both ciphertexts and proofs to dedicated servers. The homomorphic-aggregation server sums encrypted updates, while the ZKP server enforces correctness by rejecting any proof that violates the norm constraint—demonstrated by catching an intentionally malicious client. End-to-end testing confirms that the combined CKKS+ZKP pipeline preserves model accuracy and ensures both confidentiality and integrity of federated updates.
To address the absence of process-level verifiability in federated learning, a verifiable architecture, zero-knowledge proof-verified and blockchain-audited federated learning (zk-BcFed), is proposed by integrating zero-knowledge proofs with blockchain. For each local model update, a multi-constraint zero-knowledge proof is generated by the client, and verified cryptographic evidence is recorded on-chain, enabling formal verification of local training without disclosure of private data. Across benchmark datasets including SVHN, FashionMNIST, and CIFAR10, among others, enabling zero-knowledge proofs is observed to produce a negligible change in accuracy while substantially improving robustness under model poisoning attacks. Collectively, zk-BcFed safeguards the computational integrity and correctness of federated learning and provides a reliable verifiability mechanism with modest overhead.
Predictive maintenance in cross-border unmanned logistics systems (CBULS) faces persistent challenges, including data privacy, system heterogeneity, and collaborative efficiency. Existing studies that combine federated learning with blockchain address only partial aspects—such as communication or trust—but fail to effectively handle non-independent and identically distributed (non-IID) data, integrate multi-layer privacy, or design consensus mechanisms tailored to cross-border logistics. This paper proposes a predictive maintenance framework that integrates an improved FedProx algorithm with a hybrid Delegated Proof of Stake (DPoS) and Practical Byzantine Fault Tolerance (PBFT) consensus. The framework incorporates zero-knowledge proofs, fully homomorphic encryption, and local differential privacy, while employing hierarchical architecture and sharding for scalability. Simulation results show that the proposed method improves prediction accuracy by 6.9% compared with FedAvg and 3.7% compared with FedProx, enhances privacy protection by over 12%, increases system throughput by approximately 23%, and reduces transaction confirmation latency by nearly 18%. These results demonstrate that the framework provides a secure, efficient, and scalable solution for predictive maintenance in CBULS.
Social media platforms rely heavily on user interaction data to personalize content and advertisements, raising concerns regarding user privacy and data misuse. Although regulations such as the General Data Protection Regulation (GDPR) aim to address these concerns, enforcement remains under the control of the platforms themselves. To address these issues, this paper proposes a privacy-preserving recommender system that minimizes personal preference data exposure while maintaining the potential for equivalent personalization accuracy (including for advertisements) through local data processing, which could access the same amount or more of pure user data than server-side models. This system integrates a lightweight client-based machine learning model to infer user preferences locally, combined with Merkle tree-based Zero-Knowledge Proof (ZKP) scheme to anonymously authenticate user requests. The authors develop a working web app prototype and evaluate performance across a range of user devices. Results show minimal latency for the client-based model (under 5 milliseconds on most devices) and diverse proof generation times, ranging from 2.6 seconds to over 18 seconds, depending on hardware capability. Server-side verification remains consistent and fast under 250 milliseconds. Although proof generation latency remains a bottleneck for real-time applications, optimization strategies such as proof-caching, cross-application preferences synchronization, and native implementation provides a promising path toward privacy-preserving personalization in social media systems.
With the rapid expansion of digital knowledge platforms and intelligent information systems, organizations and communities are producing a vast number of unstructured knowledge data, including annotated corpora, technical diagrams, collaborative whiteboard content, and domain-specific multimedia archives. However, knowledge sharing across institutions is hindered by privacy risks, high communication overhead, and fragmented ownership of data. Federated learning promises to overcome these barriers by enabling collaborative model training without exchanging raw knowledge artifacts, but its success depends on motivating data holders to undertake the additional computational and communication costs. Most existing incentive schemes, which are based on non-cooperative game formulations, neglect unstructured interactions and communication efficiency, thereby limiting their applicability in knowledge-driven scenarios. To address these challenges, we introduce SC-NBTI, a smart contract and Nash bargaining-based incentive framework for federated learning in knowledge collaboration environments. We cast the reward allocation problem as a cooperative game, devise a heuristic algorithm to approximate the NP-hard Nash bargaining solution, and integrate a probabilistic gradient sparsification method to trim communication costs while safeguarding privacy. Experiments on the FMNIST image classification task show that SC-NBTI requires fewer training rounds while achieving 5.89% higher accuracy than the DRL-Incentive baseline.
Md Bokhtiar Al Zami, Md Raihan Uddin, Dinh C. Nguyen
Federated learning (FL) has gained popularity as a privacy-preserving method of training machine learning models on decentralized networks. However to ensure reliable operation of UAV-assisted FL systems, issues like as excessive energy consumption, communication inefficiencies, and security vulnerabilities must be solved. This paper proposes an innovative framework that integrates Digital Twin (DT) technology and Zero-Knowledge Federated Learning (zkFed) to tackle these challenges. UAVs act as mobile base stations, allowing scattered devices to train FL models locally and upload model updates for aggregation. By incorporating DT technology, our approach enables real-time system monitoring and predictive maintenance, improving UAV network efficiency. Additionally, Zero-Knowledge Proofs (ZKPs) strengthen security by allowing model verification without exposing sensitive data. To optimize energy efficiency and resource management, we introduce a dynamic allocation strategy that adjusts UAV flight paths, transmission power, and processing rates based on network conditions. Using block coordinate descent and convex optimization techniques, our method significantly reduces system energy consumption by up to 29.6% compared to conventional FL approaches. Simulation results demonstrate improved learning performance, security, and scalability, positioning this framework as a promising solution for next-generation UAV-based intelligent networks.
With the rapid development of the Internet of Things (IoT), the security and privacy of personal data has received widespread attention. Federated learning models protect personal privacy data through distributed collaborative training models, but it has been shown that personal privacy data can be inferred from uploaded parameters. Federated learning models also face the challenges of privacy leakage risk, computational inefficiency and lack of verifiability. Existing differential privacybased federated learning models and homomorphic encryptionbased federated learning models are unable to balance model accuracy and security. They also face the problem of inefficient computation of client-side local data and high communication overhead. Therefore, in this paper, we propose a federated learning framework (RGC-FL) based on Re-randomizable Garbled Circuits (RGC), which achieves a balance between privacy protection and computational efficiency through dynamic encryption and re-randomization techniques. The model updates are first encrypted at the client using the obfuscated circuits and then uploaded to the server, and then the ciphertext updates are aggregated by the re-randomization technique to avoid the leakage of the original data. Secondly, the client verifies the correctness of the server’s aggregation results by zero-knowledge proof. Finally based on DDH assumption and Kilian randomization technique to defend against hybrid attacks in dynamic input scenarios. We experimentally show that the model accuracy of RGC-FL on MNIST and CIFAR-10 datasets is 97.3% and 83.9%, respectively, which is close to plaintext federated learning and significantly outperforms the Differential Privacy (DP-FL) and Fully Homomorphic Encryption scheme (FHE-FL). In terms of efficiency, the training time for a single round is only 32% of that of FHE-FL (12.4 sec vs. 38.7 sec), and the communication overhead is reduced by $80 \%(5.2 \mathrm{MB}$ vs. 25.6 MB). This paper provides an efficient and secure solution for federated learning in highly privacy-sensitive domains and promotes the wide application of AI under compliance requirements.
Traceable ring signatures (TRSs) allow a signer to create a signature that maintains anonymity while enabling traceability if needed. It merges the characteristics of traditional ring signatures with the ability to trace signers, making it ideal for applications that demand both confidentiality and accountability. In a TRS scheme, a ring of potential signers generates a signature on a message without disclosing the actual signer’s identity. However, the identity can be traced if the signer uses the same tag for multiple signatures. This paper introduces a novel formal construction of TRS under universally composable (UC) security. We integrate verifiable random functions (VRFs) and zero-knowledge proofs for membership, employing Pedersen commitments. Our signature schemes maintain a logarithmic size while preserving the UC security guarantees. Additionally, we explore the potential to extend the property of one-time anonymity in TRS to K-time anonymity.
Decentralized Federated Learning (DFL) enables collaborative model training without a central server, but it remains vulnerable to privacy leakage because shared model updates can expose sensitive information through inversion, reconstruction, and membership inference attacks. Differential Privacy (DP) provides formal safeguards, yet existing DP-enabled DFL methods operate as black-boxes that cannot track cumulative noise added across clients and rounds, forcing each participant to inject worst-case perturbations that severely degrade accuracy. We propose PrivateDFL, a new explainable and privacy-preserving framework that addresses this gap by combining a HyperDimensional Computing (HD) model with a transparent DP noise accountant tailored to decentralized learning. HD offers structured, noise-tolerant high-dimensional representations, while the accountant explicitly tracks cumulative perturbations so each client adds only the minimal incremental noise required to satisfy its (epsilon, delta) budget. This yields significantly tighter and more interpretable privacy-utility tradeoffs than prior DP-DFL approaches. Experiments on MNIST (image), ISOLET (speech), and UCI-HAR (wearable sensor) show that PrivateDFL consistently surpasses centralized DP-SGD and Renyi-DP Transformer and deep learning baselines under both IID and non-IID partitions, improving accuracy by up to 24.4% on MNIST, over 80% on ISOLET, and 14.7% on UCI-HAR, while reducing inference latency by up to 76 times and energy consumption by up to 36 times. These results position PrivateDFL as an efficient and trustworthy solution for privacy-sensitive pattern recognition applications such as healthcare, finance, human-activity monitoring, and industrial sensing. Future work will extend the accountant to adversarial participation, heterogeneous privacy budgets, and dynamic topologies.
Mahendran Chinnaiah, A. Kumar Chandra Gupta, Saurabh Srivastava, Ashok Ghimire
At a time when data privacy laws and cyber-attacks are on the rise, Zero-Knowledge Proofs (ZKPs) and Artificial Intelligence (AI) hold the potential of a transformational paradigm of safe (privacy-preserving) machine learning (ML) inferences. In this paper, we present a new architecture that facilitates Zero-Knowledge AI, in which sensitive data inputs and internal model parameters remain unknown during the model inference procedure across distributed ecosystems. The proposed framework can help preserve privacy standards like GDPR and HIPAA, inference accuracies, and scalability of these inferences by utilising mechanisms to observe cryptographic zero-knowledge protocols, as well as federated learning protocols. We describe the construction of ZK-friendly models to apply to neural inference pipelines, efficient zk-SNARK-based model validation, decentralized trusting schemes, and privacy-respecting model auditing. Testing over a variety of healthcare and financial datasets indicates that our Zero-Knowledge AI solution results in high privacy guarantees with limited throughput losses. The work provides a strong basis on how to implement trusted and privacy-first AI systems in the real life and distributed operating environment.
R. Uma Mageswari, K. Nallarasu, L. Remegius Praveen Sahayaraj, A. A. Abd El-Aziz
Blockchain-driven Federated Learning (BFL) represents an intriguing intersection of two cutting-edge technologies: blockchain and federated learning. A form of distributed machine learning technique known as Federated Learning (FL) aims to preserve the privacy of user data. FL supports privacy preservation, decentralization, and collaborative learning by the means of retaining user data on local devices, training the models without sharing raw data, minimizing the danger of leakage of user data, and avoiding the need for centralized data storage. Beyond these attractive features held by FL, arduous challenges like ensuring secure model aggregation and communication, failure of single points, vulnerability faced by centralized parameter servers, minimal client participation due to lack of motivation, and incentives lacking are encountered. To provide a solution for these obstructions, an innovative idea is to integrate FL with blockchain, which is another decentralized cutting-edge technology. This collaboration leads to a much more robust BFL. FL can be enhanced through blockchain via data provenance where blockchain records data origins as well as model updates by using consensus mechanisms. The consensus mechanisms here ensure the decentralized model integrity, and then the Smart Contracts ensure the automated reward distribution to incentivize participation. FL and blockchain technology use cases are mostly involved in sectors like healthcare, finance, transportation, smart cities, etc. independently. These two core technologies, FL and blockchain, are constructively combined to achieve inviolable higher-end applications, which promise minimized data leakage risk in collaborative data sharing.