arXiv subjects

Linpeng Jia

Publications and source records attributed to Linpeng Jia.

3 recordsLinked to original sources

arXiv2026

XIR: A Framework for Interoperability across Cross-Chain Protocols Based on a Verifiable Intermediate Representation

Cross-chain protocols enable applications to exchange messages across blockchains. Under point-to-point configurations, communication depends on a direct connection between the source and destination blockchains, limiting blockchain reachability and requiring additional configurations to connect more blockchains. To quantify this problem, this paper analyzes approximately 25 million mainnet cross-chain transaction events collected from six protocols (Axelar, CCIP, Hyperlane, LayerZero, Relay, and Wormhole) between January and October 2025. The resulting graph covers 286 active blockchains and 11,935 directly connected ordered blockchain pairs. These connections provide a direct reachability of 14.64%, while full direct connectivity would require 81,510 point-to-point configurations. We present XIR, a framework for interoperability across cross-chain protocols based on a verifiable intermediate representation. This representation binds an application message to an ordered record of authenticated cross-chain protocol deliveries, preserving message identity and verification history across protocol boundaries. XIR Gateways and XIR Adapters use this representation to compose existing connections into same-protocol and cross-protocol multi-hop paths. We implement an XIR prototype integrating Hyperlane and LayerZero and evaluate it in local and public-testnet environments. Theoretical analysis and evaluation show that, with correctly configured cross-chain protocol connections, XIR avoids 67,018 additional point-to-point configurations, equivalent to 84.88% of the total required by a point-to-point configuration baseline serving the same reachable pairs, and increases reachability from 14.64% to 96.86% of all ordered blockchain pairs.

cs.CR↗

arXiv2026

Nautilus: A Verifiable Hierarchical Federated Learning Framework for Vehicular-Edge-Cloud Systems

Federated Learning (FL) enables privacy-preserving collaborative learning for Internet of Vehicles (IoV) scenarios, but extreme heterogeneity of vehicular-edge-cloud resources severely limits system efficiency. Dynamic scheduling strategies mitigate this issue but introduce new trust concerns: verifying fair scheduling decisions and faithful client execution of compression instructions without privacy leakage remains an open challenge. We propose Nautilus, a verifiable efficient federated learning framework. First, a multi-dimensional resource-aware scheduling algorithm dynamically allocates compression ratios and training tasks based on vehicle bandwidth, latency and computing power, improving training efficiency. Second, a Zero-Knowledge Proof (ZKP) mechanism ensures scheduling fairness and execution compliance while preserving privacy. Experiments show the framework reduces communication overhead and accelerates convergence with guaranteed system integrity.

cs.DC↗

arXiv2026

EchoFlow: A Workload-Aware Parameter Tuning Method for Blockchain Systems

Blockchain systems expose a large number of tunable parameters that significantly influence system performance. However, in practice, a single parameter configuration is often applied across different workloads, leaving substantial unexploited performance potential. To address this, we propose EchoFlow, a blockchain parameter tuning framework that adaptively adjusts parameter configurations based on workload characteristics, enabling continuous performance optimization. EchoFlow employs a distributed reinforcement learning approach in which multiple actors perform parallel sampling to mitigate the substantial time required for sample generation in blockchain environments. To further accelerate convergence, we introduce a genetic algorithm during the initial phase of training to generate high-quality samples. Extensive experimental evaluations demonstrate that EchoFlow consistently outperforms existing methods across diverse workload scenarios while also reducing training time, highlighting its effectiveness and practical value.

cs.DC↗