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Researcher profile

Huawei Li

Huawei Li contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Artificial IntelligenceCryptography and SecurityHardware ArchitectureComputer VisionDistributed, Parallel, and Cluster ComputingMachine LearningRobotics

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Published work

5 published item(s)

preprint2026arXiv

Arcane: An Assertion Reduction Framework through Semantic Clustering and MCTS-Guided Rule Exploring

Assertion-based Verification (ABV) is essential for ensuring that hardware designs conform to their intended specifications. However, existing automated assertion-generation approaches, such as LLM-based frameworks, often generate large numbers of redundant assertions, which significantly degrade simulation efficiency. To mitigate the simulation overhead caused by redundant assertions, this paper proposes Arcane, an efficient assertion reduction framework. It integrates a two-tier assertion clustering approach for accurate semantic classification of large assertion sets, and employs Monte Carlo Tree Search (MCTS) to explore optimal rule-application sequences for efficient assertion reduction. The experimental results on Assertionbench [20] show that Arcane achieves a reduction of up to 76.2% in the assertion count while fully preserving formal coverage and mutation-detection ability. Further simulation studies demonstrate a speedup of 2.6x to 6.1x speedup in simulation time. The proposed framework is released at https://anonymous.4open.science/r/Arcane1-0A6F/.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Realtime-VLA FLASH: Speculative Inference Framework for Diffusion-based VLAs

Diffusion-based vision-language-action models (dVLAs) are promising for embodied intelligence but are fundamentally limited in real-time deployment by the high latency of full inference. We propose Realtime-VLA FLASH, a speculative inference framework that eliminates most full inference calls during replanning by introducing a lightweight draft model with parallel verification via the main model's Action Expert and a phase-aware fallback mechanism that reverts to the full inference pipeline when needed. This design enables low-latency, high-frequency replanning without sacrificing reliability. Experiments show that on LIBERO, FLASH largely preserves task performance by replacing many 58.0 ms full-inference rounds with speculative rounds as fast as 7.8 ms, lowering task-level average inference latency to 19.1 ms (3.04x speedup). We additionally demonstrate effectiveness on real-world conveyor-belt sorting, highlighting its practical impact for latency-critical embodied tasks.

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preprint2022arXiv

DHSA: Efficient Doubly Homomorphic Secure Aggregation for Cross-silo Federated Learning

Secure aggregation is widely used in horizontal Federated Learning (FL), to prevent leakage of training data when model updates from data owners are aggregated. Secure aggregation protocols based on Homomorphic Encryption (HE) have been utilized in industrial cross-silo FL systems, one of the settings involved with privacy-sensitive organizations such as financial or medical, presenting more stringent requirements on privacy security. However, existing HE-based solutions have limitations in efficiency and security guarantees against colluding adversaries without a Trust Third Party. This paper proposes an efficient Doubly Homomorphic Secure Aggregation (DHSA) scheme for cross-silo FL, which utilizes multi-key Homomorphic Encryption (MKHE) and seed homomorphic pseudorandom generator (SHPRG) as cryptographic primitives. The application of MKHE provides strong security guarantees against up to $N-2$ participates colluding with the aggregator, with no TTP required. To mitigate the large computation and communication cost of MKHE, we leverage the homomorphic property of SHPRG to replace the majority of MKHE computation by computationally-friendly mask generation from SHPRG, while preserving the security. Overall, the resulting scheme satisfies the stringent security requirements of typical cross-silo FL scenarios, at the same time providing high computation and communication efficiency for practical usage. We experimentally demonstrate our scheme brings a speedup to 20$\times$ over the state-of-the-art HE-based secure aggregation, and reduces the traffic volume to approximately 1.5$\times$ inflation over the plain learning setting.

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preprint2022arXiv

Fault-Tolerant Deep Learning: A Hierarchical Perspective

With the rapid advancements of deep learning in the past decade, it can be foreseen that deep learning will be continuously deployed in more and more safety-critical applications such as autonomous driving and robotics. In this context, reliability turns out to be critical to the deployment of deep learning in these applications and gradually becomes a first-class citizen among the major design metrics like performance and energy efficiency. Nevertheless, the back-box deep learning models combined with the diverse underlying hardware faults make resilient deep learning extremely challenging. In this special session, we conduct a comprehensive survey of fault-tolerant deep learning design approaches with a hierarchical perspective and investigate these approaches from model layer, architecture layer, circuit layer, and cross layer respectively.

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preprint2022arXiv

SASH: Efficient Secure Aggregation Based on SHPRG For Federated Learning

To prevent private training data leakage in Fed?erated Learning systems, we propose a novel se?cure aggregation scheme based on seed homomor?phic pseudo-random generator (SHPRG), named SASH. SASH leverages the homomorphic property of SHPRG to simplify the masking and demask?ing scheme, which for each of the clients and for the server, entails an overhead linear w.r.t model size and constant w.r.t number of clients. We prove that even against worst-case colluding adversaries, SASH preserves training data privacy, while being resilient to dropouts without extra overhead. We experimentally demonstrate SASH significantly improves the efficiency to 20 times over baseline, especially in the more realistic case where the numbers of clients and model size become large, and a cer?tain percentage of clients drop out from the system.

0 citations0 reviewsNo code linkedOpen access