Jul 2026· Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology· 0 citations· 14 references
Abstract
This work develops a Blockchain-Enabled Federated Learning (BCFL) framework to address some of the major concerns in federated learning: the use of a central server, the lack of transparency/traceability during the training process and the risk of a malicious participant leaking private information. By utilizing the blockchain as an intermediary to aggregate all the local models trained at each participants location, the BCFL framework ensures the integrity of the aggregated model, provides a transparent record of all transactions and prevents tampering. In addition, this framework utilizes the Interplanetary File System (IPFS) to allow nodes to store their own model parameters locally and provides a decentralized method of managing those parameters. The system uses a lightweight hash-based verification mechanism that allows model updates to be verified using their Content Identifiers (CIDs) on the blockchain, without sending the raw parameters to a central authority. Tests using the MNIST, Fashion-MNIST, and CIFAR-10 datasets show that the BCFL framework improves the accuracy of the Global model in the presence of a 20% label-flipping data poisoning attack by 31.65% to 42.40% compared to standard FL baselines without outlier defense. The integration of blockchain consensus and IPFS storage causes a predictable computational training latency overhead of around 7.5%-8.2%. Yet, the framework achieves baseline predictive accuracy (on average 88.49% on CIFAR-10), which indicates a practical and quantifiable trade-off between computing and decentralized security.
A block chain-based Privacy-preserving and Secure Federated Learning (BPS-FL) system that uses threshold homomorphic encryption to safeguard the local gradients of clients in order to successfully solve such privacy and security assault challenges is suggested.
Umema Samreen, I. S. P. James· American Journal of AI Cyber...· 0 citations
Federated Learning (FL) enables distributed machine learning without sharing raw data, but its reliance on a central aggregation server introduces critical vulnerabilities gradient inversion attacks, Byzantine poisoning, free-riding by rational participants, and single-point-of-failure risk. Blockchain has been proposed as a structural remedy, giving rise to the field of Blockchain-Enabled Federated Learning (BEFL). reviews exactly eight representative peer-reviewed BEFL systems published selected to cover four core challenge dimensions: privacy, security, scalability, and incentive design. compare each system across accuracy under data heterogeneity, formal privacy guarantees, Byzantine robustness, throughput, and communication efficiency. find that every reviewed system excels on one or two dimensions while underperforming on others, and that no single published system simultaneously resolves all four challenges. Based on this review identify four fundamental research gaps and conclude with a structured research agenda. This study also reveals that the base FL optimizer contributes more to model accuracy than any blockchain or privacy mechanism a finding with significant design implications.
Raman Dubey, A. Jain, Richa Sharma· International Conference on...· 0 citations
: In recent years, the exponential rise of digital banking and online financial services often grows, the demand for secure and transparent and tamper-proof transaction systems. This paper proposes a blockchain based framework, called "Fortifying Financial Transactions with Blockchain", which aims at improving the integrity and reliability of financial operations as decentralised verification and smart contracts. The whole system utilizes smart contracts coded in Solidity enforced on a local blockchain (Ganache) to capture and to authenticate each interaction about the flow of the value between user wallets integrated using an extension called MetaMask. By using the hashing algorithm SHA-3 (keccak256) for the integrity of the exchanged data, and the transaction of data is approved by business logic on the blockchain, so it should be impossible to falsify the exchanged data to ensure immutability of the data exchanged between counterparties. In addition, an off-chain Node.js backend enables writing the block to an internal audit log, indexing, and the ability to resolve administrative disputes through compensating transactions, and a frontend built on React can give a real time view of transactions that are still pending and pending confirmations. Experimental results on the local blockchain show that the proposed approach improves the transaction traceability and auditability to a large extent, under a reasonable condition on economical consumption of gas and lesser value of transaction execution time. The system thus provides a secure, auditable and scalable model that could be used for next generation digital banking environments.
R. Midhuna, J. Biju· Proceedings of the 1st Inter...· 0 citations
In recent years, the rapid growth of distributed artificial intelligence (AI) and blockchain technology has led to new opportunities for building secure, transparent, and privacy-preserving learning systems. Federated Learning (FL) enables multiple users or organizations to collaboratively train a global AI model without sharing their private data, while Blockchain provides immutability, traceability, and decentralized trust. However, most existing blockchain-based FL systems are limited to a single network, lacking interoperability and scalability across multiple chains. This review paper explores the emerging concept of Cross-Chain Federated Learning (CCFL), which integrates federated learning with cross-chain blockchain communication to achieve secure and interoperable decentralized AI. The paper discusses existing research works, current architectures, algorithms, and cross-chain mechanisms, identifying key challenges such as model verification, communication overhead, and data integrity. Furthermore, it highlights how the proposed framework addresses these challenges by using smart contracts, cryptographic hashing, and relayer-based synchronization.The study concludes that integrating FL with cross-chain blockchain technology can significantly enhance privacy, security, and collaboration among diverse AI systems, paving the way for next-generation decentralized intelligence.
Keywords— Federated Learning, Blockchain, Cross-Chain Communication, Decentralized AI, Data Privacy, Smart Contracts, Secure Aggregation, Interoperability
Vikrant Thombare, Mahendra Sawane· International Journal of Cre...· 0 citations
A trust-based federated learning framework in which a smart contract enabled by blockchain oversees client registration, model update logging, hash-based integrity verification, trust score calculation, malicious node penalization, aggregation approval, and decentralised audit logging is proposed.
Shankar Thalla· International Journal of Lat...· 0 citations
The usage of blockchain technologies incorporates a trust layer due to its immutability and transparency for all participants. The implementation of this technology in order to validate and authenticate the identity of different entities in a Data Spaces ecosystem, allows to all the contributors to validate which of them have certain sets of traits suitable for their data sharing, interests, and moreover, role managing. This way, connectors can automatically identify with who they are interacting and choose which data to share with them based on these characteristics. Some useful examples are the detection of certain issued verifiable credentials that prove their belonging to a specific sector, accreditations … that automatically provide them with access to specific datasets. In order to achieve this, the presented Trust Layer uses a governance infrastructure with blockchain, an identity layer to store private keys and connect the blockchain address to a decentralized identifier, and a credential manager to issue and verify the Verifiable Credentials. Added to this, an integration layer is designed and implemented, which connects these components to the Data Space Connector. For this specific technical implementation, the design has been built to be operable with Eclipse Dataspace Components (EDC), given its Verifiable Credentials interoperability and its wide adoption among Data Spaces as their preferred connector. A qualitative discussion of the architecture’s strengths and challenges is provided, covering aspects such as decentralization, regulatory alignment, and adoption barriers, together with a comparison against alternative identity and trust models for federated data spaces.
Lidia Alaejos Herrero, M. R. Ruiz Molina, Diego Molinero Moreno et al.· Open Research Europe· 0 citations
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