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24th international conference on algorithms and architectures for parallel processing, ica3pp 2024

24th International Conference on Algorithms and Architecture...

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24th international conference on algorithms and architectures for parallel processing, ica3pp 2024

ISBN: (纸本)9789819615445

the proceedings contain 131 papers. the special focus in this conference is on algorithms and architectures for parallel processing. the topics include: MARO: Enabling Full MPI Automatic Refactoring in DSL-Based Programming Framework;SSC: An SRAM-Based Silence Computing Design for On-chip Memory;TP-BFT: A Faster Asynchronous BFT Consensus with parallel Structure;LTP: A Lightweight On-Chip Temporary Prefetcher for Data-Dependent Memory Accesses;A Neural Network-Based PUF Protection Method Against Machine Learning Attack;Compression Format and Systolic Array Structure Co-design for Accelerating Sparse Matrix Multiplication in DNNs;multidimensional Intrinsic Identity Construction and Dynamic Seamless Authentication Schemes in IoT Environments;invisible Backdoor Attack with Image Contours Triggers;finestra: Multi-aggregator Swarm Learning for Gradient Leakage Defense;DIsFU: Protecting Innocent Clients in Federated Unlearning;multiple-Round Aggregation of Abstract Semantics for Secure Heterogeneous Federated Learning;dynamic Privacy Protection with Large Language Model in Social Networks;a Dynamic Symmetric Searchable Encryption Scheme for Rapid Conjunctive Queries;a Data Watermark Scheme Base on Data Converted Bitmap for Data Trading;distributed Incentive Algorithm for Fine-Grained Offloading in Vehicular Ad Hoc Networks;mitigating Over-Unlearning in Machine Unlearning with Synthetic Data Augmentation;AW-YOLOv9: Adverse Weather Conditions Adaptation for UAV Detection;efficient and Privacy-Preserving Ranking-Based Federated Learning;on-Chain Dynamic Policy Evaluation for Decentralized Access Control;DPG-FairFL: A Dual-Phase GAN-Based Defense Framework Against Image-Based Fairness Data Poisoning Attacks in Federated Learning.

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24th international conference on algorithms and architectures for parallel processing, ica3pp 2024

24th International Conference on Algorithms and Architecture...

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24th international conference on algorithms and architectures for parallel processing, ica3pp 2024

ISBN: (纸本)9789819615506

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24th international conference on algorithms and architectures for parallel processing, ica3pp 2024

24th International Conference on Algorithms and Architecture...

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24th international conference on algorithms and architectures for parallel processing, ica3pp 2024

ISBN: (纸本)9789819615247

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24th international conference on algorithms and architectures for parallel processing, ica3pp 2024

24th International Conference on Algorithms and Architecture...

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24th international conference on algorithms and architectures for parallel processing, ica3pp 2024

ISBN: (纸本)9789819615278

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24th international conference on algorithms and architectures for parallel processing, ica3pp 2024

24th International Conference on Algorithms and Architecture...

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24th international conference on algorithms and architectures for parallel processing, ica3pp 2024

ISBN: (纸本)9789819615414

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24th international conference on algorithms and architectures for parallel processing, ica3pp 2024

24th International Conference on Algorithms and Architecture...

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24th international conference on algorithms and architectures for parallel processing, ica3pp 2024

ISBN: (纸本)9789819615476

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parallel Implementation of Number-theoretic Transform on GPU Clusters 24th

Parallel Implementation of Number-Theoretic Transform on G...

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24th international conference on algorithms and architectures for parallel processing, ica3pp 2024

作者： Takahashi, Daisuke Center for Computational Sciences University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba305-8577 Japan

ISBN: (纸本)9789819615414

In this paper, we propose a parallel implementation of the number-theoretic transform (NTT) on GPU clusters. the butterfly operation of the NTT can be performed using modular addition, subtraction, and multiplication. We show that the four-step fast Fourier transform algorithm can be applied to the NTT. We parallelized the four-step NTT using MPI and OpenACC on GPU clusters. We achieved a performance of over 745 giga-operations per second on 32 nodes of a GPU cluster equipped with NVIDIA H100 PCIe GPUs for a 235-point NTT with a modulus of 63 bits. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Number theory

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Towards Information Sharing Beetle Antennae Search Optimization 24th

Towards Information Sharing Beetle Antennae Search Optimizat...

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24th international conference on algorithms and architectures for parallel processing, ica3pp 2024

作者： Liu, Xuan Wang, Chenyan Liu, Wenjian Zhang, Lefeng Liu, Xianggan Gao, Yutong Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE Minzu University of China Beijing China Hainan International College of Minzu University of China Li’an International Education Innovation pilot Zone Hainan China City University of Macau China Natural Language Processing and Knowledge Graph Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9789819615278

the study of bioinformatics-based evolutionary computation has long been of significant interest within the scientific community. Beetle antennae search algorithm is widely used because of its lightweight, however, it lacks information sharing due to individual iteration in the algorithm. In this paper, we propose an innovative pheromone-based beetle antennae search algorithm, which evolves from a single iterative individual in BAS algorithm to multiple parallel iterative individuals and incorporates the pheromone sharing mechanism found in ant colony optimization algorithm. Applying the pheromone-based beetle antennae search algorithm to the virtual machine placement problem in cloud computing, we find that pheromone sharing mechanism allows the PB-BAS algorithm to exhibit superior optimization capabilities and effectively avoids convergence to local optimal. To verify the performance of the algorithm, we select other alternative algorithms and conduct a large number of comparative experiments under different experimental setups, the experimental results show the effectiveness and efficiency of our algorithm. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ant colony optimization

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Data Poisoning Attack Against Reinforcement Learning from Human Feedback in Robot Control Tasks 24th

Data Poisoning Attack Against Reinforcement Learning from H...

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24th international conference on algorithms and architectures for parallel processing, ica3pp 2024

作者： Zhou, Zihui Gao, Yutong Qi, Minfeng Wuhan430074 China Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance Ministry of Education Minzu University of China Beijing100081 China Hainan International College of Minzu University of China Li’an International Education Innovation pilot Zone Hainan572499 China City University of Macau 999078 China

ISBN: (纸本)9789819615247

Reinforcement Learning from Human Feedback (RLHF) technology provides a method for agents to learn human preferences and perform actions that satisfy human desires. this technology was originally used to complete robot control tasks in situations where it was difficult to design a reward function. However, in the process of collecting human feedback, malicious human annotators may launch attacks on RLHF, posing a significant challenge to the technology. Previous research has mostly focused on studying the harm caused by different attacks on RLHF in fine-tuning large language models (LLMs). However, our study is focused on the field of robot control. We designed two data poisoning attacks against human feedback datasets and implemented our attacks in three different offline reinforcement learning (RL) environments. the experimental results show that RLHF is vulnerable to data poisoning attacks in robot control tasks. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Federated Learning and parallel Prompt Scheduling Strategies for Large Language Models 24th

Federated Learning and Parallel Prompt Scheduling Strategie...

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24th international conference on algorithms and architectures for parallel processing, ica3pp 2024

作者： Lv, Guangtong Gu, Bruce Jia, Xiaocong Gao, Longxiang Qu, Youyang Cui, Lei Jinan China Shandong Provincial Key Laboratory of Computer Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9789819615278

Large language models have garnered significant attention and are widely utilized across different fields due to their impressive performance. However, centralized training of these models can pose privacy risks like data leakage, hindering their application and advancement. As a decentralized learning paradigm, Federated Learning(FL) can effectively address data security in large language models and has specific application scenarios. In addition, the fine-tuning process of large language models also incurs significant communication expenses and leads to the loss of computational resources. the utilization of prompt tuning in federated learning is anticipated to decrease training costs, accelerate the training process, and enhance model performance. In this paper, we propose ‘FedLLM-ppS’ to explore a novel approach for large language models within federated learning. this method address data security concerns associated with large language models while enhancing performance and efficiency. Experiments based on several datasets for various Natural Language processing(NLP) tasks demonstrate superior performance and efficiency compared to baseline methods. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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