blockchain platform developers

Abstract: Customizing Pretrained Models (PMs) for downstream tasks demands task-specific data, which burgeoning Web3 applications provide abundantly. Blockchain-powered federated learning (BCFL) offers a secure and privacy-preserving paradigm to customize PMs among self-interested and untrusted Web3 participants. Yet, existing BCFL algorithms face severe efficiency and scalability challenges when applied to large PMs, due to their substantial computational and memory requirements and the hardware constraints of many participants. This paper introduces DICE, a novel BCFL algorithm that addresses the efficiency and scalability challenges of customizing large PMs in Web3 via two sets of synergistic techniques: (i) a dynamic progressive learning scheme and a model slice verification scheme to enable a wider range of honest participants, including those unable to deploy the full model, to engage in customizing PMs, while preserving model utility; (ii) a tree-structured committee consensus protocol and an efficiency-driven incentive scheme to address the dominant bottleneck that hinders existing BCFL algorithms from scaling to a larger number of data holders for customizing data-hungry large PMs, while maintaining security. Theoretical analysis and extensive experimental results on customizing vision and language PMs demonstrate DICE's efficiency, effectiveness, and security.

Abstract: EIP-1559 has become the dominant transaction fee mechanism in permissionless blockchains, regulating scarce block space through an adaptive base fee. The design balances a trade-off between responsiveness to demand fluctuations and stability of long-term resource usage. On a single chain, this trade-off is well understood and empirically stable. In multi-chain systems, however, cross-chain flows introduce delayed economic interactions across otherwise independent fee markets. These interactions alter the control dynamics and create stability risks that existing models do not capture. Deployed systems have already polarized. Polkadot enforces conservative update rates, effectively disabling fee adaptivity and reverting the market to inefficient first-price auctions. Cosmos adopts aggressive update rates without formal guarantees. To fill this gap, we propose a control-theoretic framework that formalizes multi-chain fee markets as a nonlinear dynamical system with delayed feedback. We identify delay-amplified over-correction as the structural root of instability. Experiments show that cross-chain coupling erodes stability relative to monolithic blockchains, reducing the safe parameter space for base fee update rates by up to 84.8%. Moreover, instability propagates across chains, contaminating the otherwise stable peer chains. At Ethereum's update rate, which leaves a monolithic chain stable, the multi-chain system exhibits extreme load oscillation between full and near-empty blocks, with over half of all blocks at full capacity. Mainstream Cosmos chains already exceed their safe bounds under their default configurations, and the resulting load oscillation raises their block failure rate by an order of magnitude. Guided by this mechanism, we design a lightweight protocol-level mitigation that removes the over-correction and stabilizes dynamics in multi-chain environments.

Abstract: Federated learning (FL) enables collaborative model training without data centralization but struggles with incentive alignment, accountability, and robustness under heterogeneous non-IID data. Existing decentralized FL (DeFL) systems leverage blockchains for coordination yet face high on-chain overhead, unverifiable self-reports, and weak fairness guarantees. We present the FLAI Protocol, a trusted execution environment (TEE)-backed DeFL framework that integrates enclave-validated aggregation, sparse Peer Evaluation Graph (sPEG) scoring, and on-chain settlement. sPEG incorporates peer-based quality, class coverage, promptness, effort, and reputation into composite contribution scores. The protocol formalizes contribution evaluation and tokenized incentive distribution, ensuring verifiable fairness and resilience against free-riding and collusion. We evaluate FLAI on image, text, and healthcare datasets and demonstrate parity with FedAvg in utility while improving fairness and accountability. A case study in consumer eHealth (aggregating wearable and electronic health record data for real-world evidence generation) illustrates the protocol's applicability to privacy-critical domains where equitable rewards, auditability, and data sovereignty are essential.

Abstract: IoMT communication systems should provide origin authentication and ensure integrity protection for the messages produced by patients' devices for transmission to gateways and further processing by other hospital services. With regard to IoMT, this paper discusses tampering, impersonation, and replay attacks on messages provided as medical reports. Long-term security of the communication system is also addressed considering potential quantum attacks. For this purpose, we design a protocol that meets requirements of practical implementation and employs an ISIS like lattice-based construction combined with concepts of the blockchain technology. Patients' devices generate a canonical JSON format medical report containing a timestamp and critical information. Using a nonce, the patients create a cryptographic signature of the message with a challenge generated from hash of a message. The gateway verifies signatures utilizing the public key of a trusted ledger associated with a particular patient and ensures freshness of the transmitted message. To optimize the performance in case when many devices interact with a gateway at once, the gateway performs batch signature validation using vectorized modular linear equations. The trusted ledger uses tamper-resistant REGISTER and TRUST UPDATE entries, maintaining a trust score of each registered patient and updating the value after each ACCEPT/REJECT operation. Our prototype application demonstrates registration procedure of a new patient, signing and verifying medical reports, auditing of the ledger, and performance assessment. Based on our experiments in the prototype, we argue that, when applied to large-scale systems, batch verification is superior to simple signature validation approach.