2022 IEEE 42nd International Conference on Distributed Computing Systems Workshops (ICDCSW)最新文献_第2页

On Learning Stable Cooperation in the Iterated Prisoner's Dilemma with Paid Incentives 有偿激励下迭代囚徒困境下的学习稳定合作

2022 IEEE 42nd International Conference on Distributed Computing Systems Workshops (ICDCSW) Pub Date : 2022-07-01 DOI: 10.1109/ICDCSW56584.2022.00031

Xiyue Sun, Fabian R. Pieroth, Kyrill Schmid, M. Wirsing, Lenz Belzner

{"title":"On Learning Stable Cooperation in the Iterated Prisoner's Dilemma with Paid Incentives","authors":"Xiyue Sun, Fabian R. Pieroth, Kyrill Schmid, M. Wirsing, Lenz Belzner","doi":"10.1109/ICDCSW56584.2022.00031","DOIUrl":"https://doi.org/10.1109/ICDCSW56584.2022.00031","url":null,"abstract":"An essential step towards collective intelligence in systems comprised of multiple independent and autonomous agents is that individual decision-makers are capable of acting cooperatively. Cooperation is especially challenging in environ-ments where collective and individual rationality diverge, like in the Prisoner's Dilemma (PD), which is often used to test whether algorithms are capable of circumventing the single non-optimal Nash equilibrium. In this paper, we extend the approach “Learning to Incentivize other Learning Agents” in two ways: 1. We analyze the impact of the payoff matrices on incentive updates, as different payoff matrices could accelerate or decelerate the growth of incentives. 2. We adapt the concept of the market from “Action Markets in Deep Multi-Agent Reinforcement Learning” to iterated PD games as to trade incentives, i.e., the final revenue of the agent is the game revenue minus the incentive it provided, and propose (sufficient) conditions for reaching stable two-way cooperation under specific assumptions.","PeriodicalId":357138,"journal":{"name":"2022 IEEE 42nd International Conference on Distributed Computing Systems Workshops (ICDCSW)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132750096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

ROFL: RObust privacy preserving Federated Learning ROFL:鲁棒隐私保护联邦学习

2022 IEEE 42nd International Conference on Distributed Computing Systems Workshops (ICDCSW) Pub Date : 2022-07-01 DOI: 10.1109/ICDCSW56584.2022.00033

Nandish Chattopadhyay, Arpita Singh, A. Chattopadhyay

{"title":"ROFL: RObust privacy preserving Federated Learning","authors":"Nandish Chattopadhyay, Arpita Singh, A. Chattopadhyay","doi":"10.1109/ICDCSW56584.2022.00033","DOIUrl":"https://doi.org/10.1109/ICDCSW56584.2022.00033","url":null,"abstract":"In the modern world of connectivity, most data is generated in a de centralised way, across a multitude of platforms like mobile devices and other loT applications. This crowd sourced data, if well analyzed, can prove to be rich in insights, for different tasks. However, the issue in utilizing it lies with the consolidation of the data, which is unacceptable to most involved parties. While every participant stands to benefit from the collective use of the massive data repositories, the lack of trust between them prevents that endeavour. In this paper, we propose ROFL, which is an end-to-end robust mechanism of learning, that has been developed keeping all the trust issues in mind and addressing the necessity of privacy. We make note of the threat models that might make the participants apprehensive and design a bi-directional two-dimensional privacy preserving framework, that builds upon the state-of-the-art in differentially private federated learning. Specifically, we propose a weighted federated averaging technique for aggregation of the differentially private models generated by the participants. We are able to provide privacy guarantees without compromising on the accuracy of the machine learning task. ROFL has been tested for multiple neural architectures (VGG-16 [1] and ResNet [2]) on multiple datasets (MNIST [3], CIFAR-I0 and CIFAR-I00 [4]). On the machine learning tasks, it is able to achieve accuracies within the range of 1 % -2 % of what a model trained on the collected data would have generated, in the average case scenario. We have verified the robustness of ROFL against attacks involving sabotaging or malicious client providing erroneous models. The study on model convergence reveals how to improve the efficiency of ROFL. We also provide evidence on how ROFL is easily scalable in nature.","PeriodicalId":357138,"journal":{"name":"2022 IEEE 42nd International Conference on Distributed Computing Systems Workshops (ICDCSW)","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114418342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Human-centered Approach to make Networked Entertainment Green: A Case Study of CDN 以人为本的绿色网络娱乐:以CDN为例

2022 IEEE 42nd International Conference on Distributed Computing Systems Workshops (ICDCSW) Pub Date : 2022-07-01 DOI: 10.1109/ICDCSW56584.2022.00050

Ji Hyung Kim, Tigran Bantikyan, Nam Wook Kim, Lewis Tseng

{"title":"A Human-centered Approach to make Networked Entertainment Green: A Case Study of CDN","authors":"Ji Hyung Kim, Tigran Bantikyan, Nam Wook Kim, Lewis Tseng","doi":"10.1109/ICDCSW56584.2022.00050","DOIUrl":"https://doi.org/10.1109/ICDCSW56584.2022.00050","url":null,"abstract":"In our day to day lives, we are becoming more re-liant on clouds, which results in an excessive energy consumption by cloud infrastructures. In 2016, the US Department of Energy found that datacenters in the US made up roughly 2 % of all electricity consumption nationwide. Designing a cloud computing system that runs on sustainable energy and simultaneously satisfies increasing user demands with high availability is still an unsolved challenge. We focus on networked entertainment systems in this short paper. The first goal of this paper is to provide a broad research agenda on using a human-in-the-loop design to tackle this challenge, discussing more problems than solutions. Second, we present our design of an interactive Content Delivery Network (CDN) that is the backbone for much networked entertainment, e.g., Netflix and YouTube. Our CDN solution (i) considers user preferences and their tolerance (to unavailability) when tuning system configuration and design, and (ii) uses system metrics and availability predictions to encourage users to adopt more energy- efficient behavior. Finally, we advocate designs that integrate expertise from multiple disciplines, including Human-Computer Interaction, Machine Learning, and Cloud/Pervasive computing, and argue why cloud platforms can benefit from adopting such a holistic approach.","PeriodicalId":357138,"journal":{"name":"2022 IEEE 42nd International Conference on Distributed Computing Systems Workshops (ICDCSW)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129362395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DeepIIoT: An Explainable Deep Learning Based Intrusion Detection System for Industrial IOT 深度物联网:可解释的基于深度学习的工业物联网入侵检测系统

2022 IEEE 42nd International Conference on Distributed Computing Systems Workshops (ICDCSW) Pub Date : 2022-07-01 DOI: 10.1109/ICDCSW56584.2022.00040

M. Alani, E. Damiani, Uttam Ghosh

引用次数: 4

Augmented Regularity for Efficient Video Anomaly Detection: An edge AI application 增强规则高效视频异常检测:边缘人工智能应用

2022 IEEE 42nd International Conference on Distributed Computing Systems Workshops (ICDCSW) Pub Date : 2022-07-01 DOI: 10.1109/ICDCSW56584.2022.00037

Jiafei Liang, Zhou Yue, Feng Yang, Zhiwen Fang

{"title":"Augmented Regularity for Efficient Video Anomaly Detection: An edge AI application","authors":"Jiafei Liang, Zhou Yue, Feng Yang, Zhiwen Fang","doi":"10.1109/ICDCSW56584.2022.00037","DOIUrl":"https://doi.org/10.1109/ICDCSW56584.2022.00037","url":null,"abstract":"Video anomaly detection, as a critical edge AI application, may dramatically minimize transmission burden by transmitting just anomalous data. Traditionally, dense consecutive frames with high resolution are utilized as the input to assure that video anomaly detection performed well. Dense input with high resolution, on the other hand, will result in high computation. To address the demand of high performance with little computation on edge devices, we propose an efficient video anomaly detection based on augmented regularity with mutual learning. Sparse frames collected from every two frames with a low-resolution of 160 × 160 are used as the input to decrease processing. Generally, performance will be hampered as a result of the low-quality inputs. To improve performance, an auxiliary network is created that uses dense inputs to mine plentiful patterns from successive frames and promotes the proposed network throughout the training phase via mutual learning. Additionally, we design augmented regularity to increase scene generalization when edge devices are grouped in distributed applications with various scenes. During the training phase, the augmented regularity, which is irrelevant to the input video, is concatenated in the input as a hidden label message. The label message infers that the inputs are normal. In the inference phase, the abnormal information can be detected from the hidden label message through generated errors. Experimental results on benchmark datasets demonstrate that the proposed method can achieve the state-of-the-art at a super-real-time speed of 80fps.","PeriodicalId":357138,"journal":{"name":"2022 IEEE 42nd International Conference on Distributed Computing Systems Workshops (ICDCSW)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126685784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Inducing Defenders to Mislead an Attacking UAV Swarm 诱导防御者误导攻击的无人机群

2022 IEEE 42nd International Conference on Distributed Computing Systems Workshops (ICDCSW) Pub Date : 2022-07-01 DOI: 10.1109/ICDCSW56584.2022.00059

Jennifer Simonjan, Stefano Ricardo Probst, M. Schranz

引用次数: 1

Towards Efficient Decentralized Federated Learning 迈向高效的分散联邦学习

2022 IEEE 42nd International Conference on Distributed Computing Systems Workshops (ICDCSW) Pub Date : 2022-07-01 DOI: 10.1109/ICDCSW56584.2022.00023

C. Pappas, D. Papadopoulos, Dimitris Chatzopoulos, Eleni Panagou, S. Lalis, M. Vavalis

引用次数: 0

Combining Distributed and Central Control for Self-Adaptive Systems of Systems 系统的自适应系统的分布式与集中控制相结合

2022 IEEE 42nd International Conference on Distributed Computing Systems Workshops (ICDCSW) Pub Date : 2022-07-01 DOI: 10.1109/ICDCSW56584.2022.00030

Christian Kröher, Lea Gerling, Klaus Schmid

引用次数: 1

Organizing Committee: ICDCSW 2022 组委会:ICDCSW 2022

2022 IEEE 42nd International Conference on Distributed Computing Systems Workshops (ICDCSW) Pub Date : 2022-07-01 DOI: 10.1109/icdcsw56584.2022.00007

引用次数: 0

Cost-Effective Optimal Multi-Source Energy Management Technique in Heterogeneous Networks 异构网络中成本效益最优多源能量管理技术

2022 IEEE 42nd International Conference on Distributed Computing Systems Workshops (ICDCSW) Pub Date : 2022-07-01 DOI: 10.1109/ICDCSW56584.2022.00042

Wanying Guo, D. Shin, Isma Farah Siddiqui, Jahwan Koo, Nawab Muhammad Faseeh Qureshi

引用次数: 2