arXiv subjects

Shanshan Han

Publications and source records attributed to Shanshan Han.

At least 19 recordsLinked to original sources

arXiv2026

Don't Stir the Pot! Authorized Vector Data Retrieval via Access-Aware Indexing

Vector databases increasingly enforce role-based access control, where each top-k approximate nearest neighbor query must return only vectors the querying role is authorized to access. Two extremes bracket the design space. A single global index built over all vectors avoids duplication but wastes search effort on unauthorized vectors and degrades recall, while an oracle index, built with all authorized vectors to the query roles, searches only authorized vectors but duplicates every shared vector between roles or queries. We present Veda and its efficient variant EffVeda, two indexing strategies built on an access-aware lattice to address access control in vector databases. The methods first partitions the dataset into disjoint data blocks by role combination, then leverage the structure of the access-aware lattice to apply copy and merge operations to group co-accessed blocks under a user-specified storage budget. Large nodes in the lattice are then indexed with HNSW, while small nodes are retained for linear scan. To facilitate query processing on the lattice, our methods construct a query plan that selects the minimal set of nodes that covers all authorized data for each role. At query time, coordinated search first queries pure (authorized-only) nodes to populate a global top-k heap, then leverages the resulting distance bound of the k-th data in the heap to prune exploration on impure nodes. Evaluations show that our methods deliver higher throughput at high recall while closely tracking the storage budget.

cs.DB↗

arXiv2025

Ultrafast giant enhancement of second harmonic generation in a strongly correlated cobaltite

In quantum materials, nonlinear optical responses are highly sensitive to electronic structure and many-body interactions. Probing and manipulating such nonlinear processes is a complex and subtle endeavor, yet it offers deep insights into emerging physics and functionalities. Here, we report an anomalous ultrafast enhancement of second harmonic generation (SHG) in a strongly correlated cobaltite YbBaCo$_4$O$_7$. Above-bandgap femtosecond pumping increases SHG intensity by up to 60 % within 200 fs, with the enhancement persisting for tens of picoseconds. The enhancement is strongly anisotropic, with substantial amplification of in-plane susceptibility tensors, whereas the out-of-plane counterpart shows negligible change. We attribute these anomalies to ultrafast photodoping-induced modulation of the on-site Coulomb repulsion, which dynamically renormalizes the band structure and selectively amplifies specific $\chi^{(2)}$ components. These nonlinear dynamics encode rich information about the orbital symmetries and energies of the states involved, opening new avenues for all-optical probing of electronic structure in strongly correlated materials.

cond-mat.str-el↗

arXiv2025

Mn4Al11: A Half-Semimetal Candidate with Anomalous Electronic Behaviors

Half-semimetals, characterized by their spin-polarized electronic states, hold significant promise for spintronic applications but remain scarce due to stringent electronic and magnetic criteria. Through a combination of transport measurements and optical spectroscopy, we investigated the intermetallic compound Mn4Al11, which features an exceptionally low carrier concentration and undergoes a magnetic phase transition near 68 K. Transport measurements reveal anomalies that deviate from typical metallic behavior at low temperatures. Optical spectroscopy indicates a small, nearly frequency-independent optical conductivity in the far-infrared region, with spectral weight decreasing as the temperature drops from 300 K to 50 K. These behaviors suggest a temperaturedependent carrier density and significant scattering of charge carriers. Combining experimental findings with calculated electronic band structures, we propose that Mn4Al11 is a novel half-semimetal candidate exhibiting a ferrimagnetic ground state.

cond-mat.str-el↗

arXiv2025

Bridging the Safety Gap: A Guardrail Pipeline for Trustworthy LLM Inferences

We present Wildflare GuardRail, a guardrail pipeline designed to enhance the safety and reliability of Large Language Model (LLM) inferences by systematically addressing risks across the entire processing workflow. Wildflare GuardRail integrates several core functional modules, including Safety Detector that identifies unsafe inputs and detects hallucinations in model outputs while generating root-cause explanations, Grounding that contextualizes user queries with information retrieved from vector databases, Customizer that adjusts outputs in real time using lightweight, rule-based wrappers, and Repairer that corrects erroneous LLM outputs using hallucination explanations provided by Safety Detector. Results show that our unsafe content detection model in Safety Detector achieves comparable performance with OpenAI API, though trained on a small dataset constructed with several public datasets. Meanwhile, the lightweight wrappers can address malicious URLs in model outputs in 1.06s per query with 100% accuracy without costly model calls. Moreover, the hallucination fixing model demonstrates effectiveness in reducing hallucinations with an accuracy of 80.7%.

cs.AI↗

arXiv2024

Fox-1: Open Small Language Model for Cloud and Edge

We present Fox-1, a series of small language models (SLMs) consisting of Fox-1-1.6B and Fox-1-1.6B-Instruct-v0.1. These models are pre-trained on 3 trillion tokens of web-scraped document data and fine-tuned with 5 billion tokens of instruction-following and multi-turn conversation data. Aiming to improve the pre-training efficiency, Fox-1-1.6B model introduces a novel 3-stage data curriculum across all the training data with 2K-8K sequence length. In architecture design, Fox-1 features a deeper layer structure, an expanded vocabulary, and utilizes Grouped Query Attention (GQA), offering a performant and efficient architecture compared to other SLMs. Fox-1 achieves better or on-par performance in various benchmarks compared to StableLM-2-1.6B, Gemma-2B, Qwen1.5-1.8B, and OpenELM1.1B, with competitive inference speed and throughput. The model weights have been released under the Apache 2.0 license, where we aim to promote the democratization of LLMs and make them fully accessible to the whole open-source community.

cs.CL↗

arXiv2024

Alopex: A Computational Framework for Enabling On-Device Function Calls with LLMs

The rapid advancement of Large Language Models (LLMs) has led to their increased integration into mobile devices for personalized assistance, which enables LLMs to call external API functions to enhance their performance. However, challenges such as data scarcity, ineffective question formatting, and catastrophic forgetting hinder the development of on-device LLM agents. To tackle these issues, we propose Alopex, a framework that enables precise on-device function calls using the Fox LLM. Alopex introduces a logic-based method for generating high-quality training data and a novel ``description-question-output'' format for fine-tuning, reducing risks of function information leakage. Additionally, a data mixing strategy is used to mitigate catastrophic forgetting, combining function call data with textbook datasets to enhance performance in various tasks. Experimental results show that Alopex improves function call accuracy and significantly reduces catastrophic forgetting, providing a robust solution for integrating function call capabilities into LLMs without manual intervention.

cs.AI↗

arXiv2024

Bridging Today and the Future of Humanity: AI Safety in 2024 and Beyond

The advancements in generative AI inevitably raise concerns about their risks and safety implications, which, in return, catalyzes significant progress in AI safety. However, as this field continues to evolve, a critical question arises: are our current efforts on AI safety aligned with the advancements of AI as well as the long-term goal of human civilization? This paper presents a blueprint for an advanced human society and leverages this vision to guide current AI safety efforts. It outlines a future where the Internet of Everything becomes reality, and creates a roadmap of significant technological advancements towards this envisioned future. For each stage of the advancements, this paper forecasts potential AI safety issues that humanity may face. By projecting current efforts against this blueprint, this paper examines the alignment between the current efforts and the long-term needs, and highlights unique challenges and missions that demand increasing attention from AI safety practitioners in the 2020s. This vision paper aims to offer a broader perspective on AI safety, emphasizing that our current efforts should not only address immediate concerns but also anticipate potential risks in the expanding AI landscape, thereby promoting a safe and sustainable future of AI and human civilization.

cs.CY↗

arXiv2024

ScaleLLM: A Resource-Frugal LLM Serving Framework by Optimizing End-to-End Efficiency

Large language models (LLMs) have surged in popularity and are extensively used in commercial applications, where the efficiency of model serving is crucial for the user experience. Most current research focuses on optimizing individual sub-procedures, e.g. local inference and communication, however, there is no comprehensive framework that provides a holistic system view for optimizing LLM serving in an end-to-end manner. In this work, we conduct a detailed analysis to identify major bottlenecks that impact end-to-end latency in LLM serving systems. Our analysis reveals that a comprehensive LLM serving endpoint must address a series of efficiency bottlenecks that extend beyond LLM inference. We then propose ScaleLLM, an optimized system for resource-efficient LLM serving. Our extensive experiments reveal that with 64 concurrent requests, ScaleLLM achieves a 4.3x speed up over vLLM and outperforms state-of-the-arts with 1.5x higher throughput.

cs.DC↗

arXiv2024

TorchOpera: A Compound AI System for LLM Safety

We introduce TorchOpera, a compound AI system for enhancing the safety and quality of prompts and responses for Large Language Models. TorchOpera ensures that all user prompts are safe, contextually grounded, and effectively processed, while enhancing LLM responses to be relevant and high quality. TorchOpera utilizes the vector database for contextual grounding, rule-based wrappers for flexible modifications, and specialized mechanisms for detecting and adjusting unsafe or incorrect content. We also provide a view of the compound AI system to reduce the computational cost. Extensive experiments show that TorchOpera ensures the safety, reliability, and applicability of LLMs in real-world settings while maintaining the efficiency of LLM responses.

cs.AI↗

arXiv2024

LLM Multi-Agent Systems: Challenges and Open Problems

This paper explores multi-agent systems and identify challenges that remain inadequately addressed. By leveraging the diverse capabilities and roles of individual agents, multi-agent systems can tackle complex tasks through agent collaboration. We discuss optimizing task allocation, fostering robust reasoning through iterative debates, managing complex and layered context information, and enhancing memory management to support the intricate interactions within multi-agent systems. We also explore potential applications of multi-agent systems in blockchain systems to shed light on their future development and application in real-world distributed systems.

cs.MA↗

arXiv2023

Hiding Access-pattern is Not Enough! Veil: A Storage and Communication Efficient Volume-Hiding Algorithm

This paper addresses volume leakage (i.e., leakage of the number of records in the answer set) when processing keyword queries in encrypted key-value (KV) datasets. Volume leakage, coupled with prior knowledge about data distribution and/or previously executed queries, can reveal both ciphertexts and current user queries. We develop a solution to prevent volume leakage, entitled Veil, that partitions the dataset by randomly mapping keys to a set of equi-sized buckets. Veil provides a tunable mechanism for data owners to explore a trade-off between storage and communication overheads. To make buckets indistinguishable to the adversary, Veil uses a novel padding strategy that allow buckets to overlap, reducing the need to add fake records. Both theoretical and experimental results show Veil to significantly outperform existing state-of-the-art.

cs.DB↗

arXiv2023

Room-temperature non-volatile optical manipulation of polar order in a charge density wave

Utilizing ultrafast light-matter interaction to manipulate electronic states of quantum materials is an emerging area of research in condensed matter physics. It has significant implications for the development of future ultrafast electronic devices. However, the ability to induce long-lasting metastable electronic states in a fully reversible manner is a long-standing challenge.Here, by using ultrafast laser excitations, we demonstrate the capability to manipulate the electronic polar states in the charge-density-wavematerial EuTe4 in a non-volatile manner. The process is completely reversible and is achieved at room temperature with an all-optical approach. Each induced non-volatile state brings about modifications to the electrical resistance and second harmonic generation intensity. The results point to layer-specific phase inversion dynamics by which photoexcitation mediates the stacking polar order of the system. Our findings extend the scope of non-volatile all-optical control of electronic states to ambient conditions, and highlight a distinct role of layerdependent phase manipulation in quasi-two-dimensional systems with inherent sublayer stacking orders.

cond-mat.str-el↗

arXiv2023

Kick Bad Guys Out! Conditionally Activated Anomaly Detection in Federated Learning with Zero-Knowledge Proof Verification

Federated Learning (FL) systems are susceptible to adversarial attacks, such as model poisoning attacks and backdoor attacks. Existing defense mechanisms face critical limitations in deployments, such as relying on impractical assumptions (e.g., adversaries acknowledging the presence of attacks before attacking) or undermining accuracy in model training, even in benign scenarios. To address these challenges, we propose CustodianFL, a two-staged anomaly detection method specifically designed for FL deployments. In the first stage, it flags suspicious client activities. In the second stage that is activated only when needed, it further examines these candidates using Three-Sigma Rule to identify and exclude truly malicious local models from FL training. To ensure integrity and transparency within the FL system, CustodianFL integrates zero-knowledge proofs, enabling clients to cryptographically verify the server's detection process without relying on the server's goodwill. CustodianFL operates without unrealistic assumptions and avoids interfering with FL training in attack-free scenarios. It bridges the gap between theoretical advances in FL security and the practical demands of real FL systems. Experimental results demonstrate that CustodianFL consistently delivers performance comparable to benign cases, highlighting its effectiveness in identifying and eliminating malicious models with high accuracy.

cs.CR↗

arXiv2023

Giant nonlinear optical wave mixing in van der Waals compound MnPSe3

Optical nonlinearities, one of the most fascinating properties of two-dimensional (2D) materials, are essential for exploring novel physics in 2D systems and developing next-generation nonlinear optical applications. While tremendous efforts have been made to discover and optimize second-order nonlinear optical responses in various 2D materials, higher odd-order nonlinear processes, which are in general much less efficient than second order ones, have been paid less attention despite their scientific and applicational significance. Here we report giant odd-order nonlinear optical wave mixing in a correlated van der Waals insulator MnPSe3 at room temperature. Illuminated by two near-infrared femtosecond lasers simultaneously, it generates a series of degenerate and non-degenerate four- and six-wave mixing outputs, with conversion efficiencies up to the order of $10^{-4}$ and $10^{-6}$ for the four- and six-wave mixing processes, respectively, far exceeding the efficiencies of several prototypical nonlinear optical materials (GaSe, LiNbO3). This work highlights the intriguing prospect of transition metal phosphorous trichalcogenides for future research of the nonlinear light matter interactions in 2D systems and for potential nonlinear photonic applications.

physics.optics↗

arXiv2023

Privacy-Preserving Federated Heavy Hitter Analytics for Non-IID Data

Federated heavy-hitter analytics involves the identification of the most frequent items within distributed data. Existing methods for this task often encounter challenges such as compromising privacy or sacrificing utility. To address these issues, we introduce a novel privacy-preserving algorithm that exploits the hierarchical structure to discover local and global heavy hitters in non-IID data by utilizing perturbation and similarity techniques. We conduct extensive evaluations on both synthetic and real datasets to validate the effectiveness of our approach. We also present FedCampus, a demonstration application to showcase the capabilities of our algorithm in analyzing population statistics.

cs.DC↗

arXiv2023

FedSecurity: Benchmarking Attacks and Defenses in Federated Learning and Federated LLMs

This paper introduces FedSecurity, an end-to-end benchmark that serves as a supplementary component of the FedML library for simulating adversarial attacks and corresponding defense mechanisms in Federated Learning (FL). FedSecurity eliminates the need for implementing the fundamental FL procedures, e.g., FL training and data loading, from scratch, thus enables users to focus on developing their own attack and defense strategies. It contains two key components, including FedAttacker that conducts a variety of attacks during FL training, and FedDefender that implements defensive mechanisms to counteract these attacks. FedSecurity has the following features: i) It offers extensive customization options to accommodate a broad range of machine learning models (e.g., Logistic Regression, ResNet, and GAN) and FL optimizers (e.g., FedAVG, FedOPT, and FedNOVA); ii) it enables exploring the effectiveness of attacks and defenses across different datasets and models; and iii) it supports flexible configuration and customization through a configuration file and some APIs. We further demonstrate FedSecurity's utility and adaptability through federated training of Large Language Models (LLMs) to showcase its potential on a wide range of complex applications.

cs.CR↗

arXiv2023

FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System

Federated Learning trains machine learning models on distributed devices by aggregating local model updates instead of local data. However, privacy concerns arise as the aggregated local models on the server may reveal sensitive personal information by inversion attacks. Privacy-preserving methods, such as homomorphic encryption (HE), then become necessary for FL training. Despite HE's privacy advantages, its applications suffer from impractical overheads, especially for foundation models. In this paper, we present FedML-HE, the first practical federated learning system with efficient HE-based secure model aggregation. FedML-HE proposes to selectively encrypt sensitive parameters, significantly reducing both computation and communication overheads during training while providing customizable privacy preservation. Our optimized system demonstrates considerable overhead reduction, particularly for large foundation models (e.g., ~10x reduction for ResNet-50, and up to ~40x reduction for BERT), demonstrating the potential for scalable HE-based FL deployment.

cs.LG↗

arXiv2023

Proof-of-Contribution-Based Design for Collaborative Machine Learning on Blockchain

We consider a project (model) owner that would like to train a model by utilizing the local private data and compute power of interested data owners, i.e., trainers. Our goal is to design a data marketplace for such decentralized collaborative/federated learning applications that simultaneously provides i) proof-of-contribution based reward allocation so that the trainers are compensated based on their contributions to the trained model; ii) privacy-preserving decentralized model training by avoiding any data movement from data owners; iii) robustness against malicious parties (e.g., trainers aiming to poison the model); iv) verifiability in the sense that the integrity, i.e., correctness, of all computations in the data market protocol including contribution assessment and outlier detection are verifiable through zero-knowledge proofs; and v) efficient and universal design. We propose a blockchain-based marketplace design to achieve all five objectives mentioned above. In our design, we utilize a distributed storage infrastructure and an aggregator aside from the project owner and the trainers. The aggregator is a processing node that performs certain computations, including assessing trainer contributions, removing outliers, and updating hyper-parameters. We execute the proposed data market through a blockchain smart contract. The deployed smart contract ensures that the project owner cannot evade payment, and honest trainers are rewarded based on their contributions at the end of training. Finally, we implement the building blocks of the proposed data market and demonstrate their applicability in practical scenarios through extensive experiments.

cs.CR↗