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Researcher profile

Xuechao Wang

Xuechao Wang contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Cryptography and SecurityDistributed, Parallel, and Cluster ComputingInformation TheoryMachine Learningmath.IT

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Published work

6 published item(s)

preprint2026arXiv

Angelfish: Leader, DAG, or Anywhere in Between

To maximize performance, many modern blockchain systems rely on eventually-synchronous, Byzantine fault-tolerant (BFT) consensus protocols. Two protocol designs have emerged in this space: protocols that minimize latency using a leader that drives both data dissemination and consensus, and protocols that maximize throughput using a separate, asynchronous data dissemination layer. Recent protocols such as Partially-Synchronous Bullshark and Sailfish combine elements of both approaches by using a DAG to enable parallel data dissemination and a leader that paces DAG formation. This improves latency while achieving state-of-the-art throughput. However, the DAG-formation process of those protocols imposes overheads that prevent matching the latency possible with a leader-based protocol. We present Angelfish, a hybrid protocol that adapts smoothly across this design space, from leader-based to DAG-based consensus. Angelfish lets a dynamically-adjusted subset of parties use best-effort broadcast to issue lightweight votes instead of using a costlier reliably broadcast to create DAG vertices. This reduces communication, tolerates more lagging nodes, and lowers latency in practice compared to prior DAG-based protocols. Our empirical evaluation shows that Angelfish attains state-of-the-art peak throughput while matching the latency of leader-based protocols under moderate throughput, delivering the best of both worlds. The implementation is open-sourced and publicly available.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Heterogeneous Tasks Offloading in Vehicular Edge Computing: A Federated Meta Deep Reinforcement Learning Approach

Vehicular edge computing (VEC) enables latency-sensitive vehicular applications by offloading computation-intensive tasks to nearby edge servers. However, real-world vehicular workloads are typically modeled as heterogeneous directed acyclic graph (DAG) tasks with complex dependency structures, making joint offloading and resource allocation highly challenging. Moreover, distributed MEC deployment raises privacy concerns when collaboratively training learning-based policies. In this paper, we propose a Federated Meta Deep Reinforcement Learning framework with GAT-Seq2Seq modeling (FedMAGS) for heterogeneous task offloading in VEC systems. The proposed approach leverages Graph Attention Networks to capture DAG dependencies, a Seq2Seq-based policy to generate structured offloading decisions, and federated meta-learning to enable fast adaptation across distributed MEC servers without sharing raw data. Extensive simulations demonstrate that FedMAGS achieves faster convergence, lower execution delay, and better scalability compared with state-of-the-art baselines. In addition, the federated design preserves data privacy while reducing communication overhead, making the framework well suited for dynamic and large-scale VEC environments.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Minotaur: Multi-Resource Blockchain Consensus

Resource-based consensus is the backbone of permissionless distributed ledger systems. The security of such protocols relies fundamentally on the level of resources actively engaged in the system. The variety of different resources (and related proof protocols, some times referred to as PoX in the literature) raises the fundamental question whether it is possible to utilize many of them in tandem and build multi-resource consensus protocols. The challenge in combining different resources is to achieve fungibility between them, in the sense that security would hold as long as the cumulative adversarial power across all resources is bounded. In this work, we put forth Minotaur, a multi-resource blockchain consensus protocol that combines proof-of-work (PoW) and proof-of-stake (PoS), and we prove it optimally fungible. At the core of our design, Minotaur operates in epochs while continuously sampling the active computational power to provide a fair exchange between the two resources, work and stake. Further, we demonstrate the ability of Minotaur to handle a higher degree of work fluctuation as compared to the Bitcoin blockchain; we also generalize Minotaur to any number of resources. We demonstrate the simplicity of Minotaur via implementing a full stack client in Rust (available open source). We use the client to test the robustness of Minotaur to variable mining power and combined work/stake attacks and demonstrate concrete empirical evidence towards the suitability of Minotaur to serve as the consensus layer of a real-world blockchain.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

The Twelvefold Way of Non-Sequential Lossless Compression

Many information sources are not just sequences of distinguishable symbols but rather have invariances governed by alternative counting paradigms such as permutations, combinations, and partitions. We consider an entire classification of these invariances called the twelvefold way in enumerative combinatorics and develop a method to characterize lossless compression limits. Explicit computations for all twelve settings are carried out for i.i.d. uniform and Bernoulli distributions. Comparisons among settings provide quantitative insight.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Everything is a Race and Nakamoto Always Wins

Nakamoto invented the longest chain protocol, and claimed its security by analyzing the private double-spend attack, a race between the adversary and the honest nodes to grow a longer chain. But is it the worst attack? We answer the question in the affirmative for three classes of longest chain protocols, designed for different consensus models: 1) Nakamoto's original Proof-of-Work protocol; 2) Ouroboros and SnowWhite Proof-of-Stake protocols; 3) Chia Proof-of-Space protocol. As a consequence, exact characterization of the maximum tolerable adversary power is obtained for each protocol as a function of the average block time normalized by the network delay. The security analysis of these protocols is performed in a unified manner by a novel method of reducing all attacks to a race between the adversary and the honest nodes.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Proof-of-Stake Longest Chain Protocols: Security vs Predictability

The Nakamoto longest chain protocol is remarkably simple and has been proven to provide security against any adversary with less than 50% of the total hashing power. Proof-of-stake (PoS) protocols are an energy efficient alternative; however existing protocols adopting Nakamoto's longest chain design achieve provable security only by allowing long-term predictability (which have serious security implications). In this paper, we prove that a natural longest chain PoS protocol with similar predictability as Nakamoto's PoW protocol can achieve security against any adversary with less than 1/(1+e) fraction of the total stake. Moreover we propose a new family of longest chain PoS protocols that achieve security against a 50% adversary, while only requiring short-term predictability. Our proofs present a new approach to analyzing the formal security of blockchains, based on a notion of adversary-proof convergence.

0 citations0 reviewsNo code linkedOpen access