IEEE journal on selected areas in information theory最新文献_第3页

Erratum to “LightVeriFL: A Lightweight and Verifiable Secure Aggregation for Federated Learning” LightVeriFL：用于联合学习的轻量级可验证安全聚合"

IEEE journal on selected areas in information theory Pub Date : 2024-08-26 DOI: 10.1109/JSAIT.2024.3413928

Baturalp Buyukates;Jinhyun So;Hessam Mahdavifar;Salman Avestimehr

引用次数: 0

JPEG Compliant Compression for DNN Vision 符合 JPEG 标准的 DNN Vision 压缩技术

IEEE journal on selected areas in information theory Pub Date : 2024-07-04 DOI: 10.1109/JSAIT.2024.3422011

Ahmed H. Salamah;Kaixiang Zheng;Linfeng Ye;En-Hui Yang

{"title":"JPEG Compliant Compression for DNN Vision","authors":"Ahmed H. Salamah;Kaixiang Zheng;Linfeng Ye;En-Hui Yang","doi":"10.1109/JSAIT.2024.3422011","DOIUrl":"https://doi.org/10.1109/JSAIT.2024.3422011","url":null,"abstract":"Conventional image compression techniques are primarily developed for the human visual system. However, with the extensive use of deep neural networks (DNNs) for computer vision, more and more images will be consumed by DNN-based intelligent machines, which makes it crucial to develop image compression techniques customized for DNN vision while being JPEG compliant. In this paper, we revisit the JPEG rate distortion theory for DNN vision. First, we propose a novel distortion measure, dubbed the sensitivity weighted error (SWE), for DNN vision. Second, we incorporate SWE into the soft decision quantization (SDQ) process of JPEG to trade SWE for rate. Finally, we develop an algorithm, called OptS, for designing optimal quantization tables for the luminance channel and chrominance channels, respectively. To test the performance of the resulting DNN-oriented compression framework and algorithm, experiments of image classification are conducted on the ImageNet dataset for four prevalent DNN models. Results demonstrate that our proposed framework and algorithm achieve better rate-accuracy (R-A) performance than the default JPEG. For some DNN models, our proposed framework and algorithm provide a significant reduction in the compression rate up to 67.84% with no accuracy loss compared to the default JPEG.","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"5 ","pages":"520-533"},"PeriodicalIF":0.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141966001","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

Throughput and Latency Analysis for Line Networks With Outage Links 带中断链路的线路网络吞吐量和延迟分析

IEEE journal on selected areas in information theory Pub Date : 2024-06-25 DOI: 10.1109/JSAIT.2024.3419054

Yanyan Dong;Shenghao Yang;Jie Wang;Fan Cheng

引用次数: 0

Addressing GAN Training Instabilities via Tunable Classification Losses 通过可调分类损失解决 GAN 训练不稳定性问题

IEEE journal on selected areas in information theory Pub Date : 2024-06-19 DOI: 10.1109/JSAIT.2024.3415670

Monica Welfert;Gowtham R. Kurri;Kyle Otstot;Lalitha Sankar

{"title":"Addressing GAN Training Instabilities via Tunable Classification Losses","authors":"Monica Welfert;Gowtham R. Kurri;Kyle Otstot;Lalitha Sankar","doi":"10.1109/JSAIT.2024.3415670","DOIUrl":"https://doi.org/10.1109/JSAIT.2024.3415670","url":null,"abstract":"Generative adversarial networks (GANs), modeled as a zero-sum game between a generator (G) and a discriminator (D), allow generating synthetic data with formal guarantees. Noting that D is a classifier, we begin by reformulating the GAN value function using class probability estimation (CPE) losses. We prove a two-way correspondence between CPE loss GANs and f-GANs which minimize f-divergences. We also show that all symmetric f-divergences are equivalent in convergence. In the finite sample and model capacity setting, we define and obtain bounds on estimation and generalization errors. We specialize these results to \u0000<inline-formula> <tex-math>$alpha $ </tex-math></inline-formula>\u0000-GANs, defined using \u0000<inline-formula> <tex-math>$alpha $ </tex-math></inline-formula>\u0000-loss, a tunable CPE loss family parametrized by \u0000<inline-formula> <tex-math>$alpha in (0,infty $ </tex-math></inline-formula>\u0000]. We next introduce a class of dual-objective GANs to address training instabilities of GANs by modeling each player’s objective using \u0000<inline-formula> <tex-math>$alpha $ </tex-math></inline-formula>\u0000-loss to obtain \u0000<inline-formula> <tex-math>$(alpha _{D},alpha _{G})$ </tex-math></inline-formula>\u0000-GANs. We show that the resulting non-zero sum game simplifies to minimizing an f-divergence under appropriate conditions on \u0000<inline-formula> <tex-math>$(alpha _{D},alpha _{G})$ </tex-math></inline-formula>\u0000. Generalizing this dual-objective formulation using CPE losses, we define and obtain upper bounds on an appropriately defined estimation error. Finally, we highlight the value of tuning \u0000<inline-formula> <tex-math>$(alpha _{D},alpha _{G})$ </tex-math></inline-formula>\u0000 in alleviating training instabilities for the synthetic 2D Gaussian mixture ring as well as the large publicly available Celeb-A and LSUN Classroom image datasets.","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"5 ","pages":"534-553"},"PeriodicalIF":0.0,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965916","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

Long-Term Fairness in Sequential Multi-Agent Selection With Positive Reinforcement 带正向强化的连续多代理选择中的长期公平性

IEEE journal on selected areas in information theory Pub Date : 2024-06-18 DOI: 10.1109/JSAIT.2024.3416078

Bhagyashree Puranik;Ozgur Guldogan;Upamanyu Madhow;Ramtin Pedarsani

{"title":"Long-Term Fairness in Sequential Multi-Agent Selection With Positive Reinforcement","authors":"Bhagyashree Puranik;Ozgur Guldogan;Upamanyu Madhow;Ramtin Pedarsani","doi":"10.1109/JSAIT.2024.3416078","DOIUrl":"https://doi.org/10.1109/JSAIT.2024.3416078","url":null,"abstract":"While much of the rapidly growing literature on fair decision-making focuses on metrics for one-shot decisions, recent work has raised the intriguing possibility of designing sequential decision-making to positively impact long-term social fairness. In selection processes such as college admissions or hiring, biasing slightly towards applicants from under-represented groups is hypothesized to provide positive feedback that increases the pool of under-represented applicants in future selection rounds, thus enhancing fairness in the long term. In this paper, we examine this hypothesis and its consequences in a setting in which multiple agents are selecting from a common pool of applicants. We propose the Multi-agent Fair-Greedy policy, that balances greedy score maximization and fairness. Under this policy, we prove that the resource pool and the admissions converge to a long-term fairness target set by the agents when the score distributions across the groups in the population are identical. We provide empirical evidence of existence of equilibria under non-identical score distributions through synthetic and adapted real-world datasets. We then sound a cautionary note for more complex applicant pool evolution models, under which uncoordinated behavior by the agents can cause negative reinforcement, leading to a reduction in the fraction of under-represented applicants. Our results indicate that, while positive reinforcement is a promising mechanism for long-term fairness, policies must be designed carefully to be robust to variations in the evolution model, with a number of open issues that remain to be explored by algorithm designers, social scientists, and policymakers.","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"5 ","pages":"424-441"},"PeriodicalIF":0.0,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141624148","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

Controlled Privacy Leakage Propagation Throughout Overlapping Grouped Learning 通过重叠分组学习控制隐私泄露传播

IEEE journal on selected areas in information theory Pub Date : 2024-06-18 DOI: 10.1109/JSAIT.2024.3416089

Shahrzad Kiani;Franziska Boenisch;Stark C. Draper

{"title":"Controlled Privacy Leakage Propagation Throughout Overlapping Grouped Learning","authors":"Shahrzad Kiani;Franziska Boenisch;Stark C. Draper","doi":"10.1109/JSAIT.2024.3416089","DOIUrl":"https://doi.org/10.1109/JSAIT.2024.3416089","url":null,"abstract":"Federated Learning (FL) is the standard protocol for collaborative learning. In FL, multiple workers jointly train a shared model. They exchange model updates calculated on their data, while keeping the raw data itself local. Since workers naturally form groups based on common interests and privacy policies, we are motivated to extend standard FL to reflect a setting with multiple, potentially overlapping groups. In this setup where workers can belong and contribute to more than one group at a time, complexities arise in understanding privacy leakage and in adhering to privacy policies. To address the challenges, we propose differential private overlapping grouped learning (DP-OGL), a novel method to implement privacy guarantees within overlapping groups. Under the honest-but-curious threat model, we derive novel privacy guarantees between arbitrary pairs of workers. These privacy guarantees describe and quantify two key effects of privacy leakage in DP-OGL: propagation delay, i.e., the fact that information from one group will leak to other groups only with temporal offset through the common workers and information degradation, i.e., the fact that noise addition over model updates limits information leakage between workers. Our experiments show that applying DP-OGL enhances utility while maintaining strong privacy compared to standard FL setups.","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"5 ","pages":"442-463"},"PeriodicalIF":0.0,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141624126","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

Information Velocity of Cascaded Gaussian Channels With Feedback 带反馈的级联高斯信道的信息速度

IEEE journal on selected areas in information theory Pub Date : 2024-06-18 DOI: 10.1109/JSAIT.2024.3416310

Elad Domanovitz;Anatoly Khina;Tal Philosof;Yuval Kochman

{"title":"Information Velocity of Cascaded Gaussian Channels With Feedback","authors":"Elad Domanovitz;Anatoly Khina;Tal Philosof;Yuval Kochman","doi":"10.1109/JSAIT.2024.3416310","DOIUrl":"https://doi.org/10.1109/JSAIT.2024.3416310","url":null,"abstract":"We consider a line network of nodes, connected by additive white noise channels, equipped with local feedback. We study the velocity at which information spreads over this network. For transmission of a data packet, we give an explicit positive lower bound on the velocity, for any packet size. Furthermore, we consider streaming, that is, transmission of data packets generated at a given average arrival rate. We show that a positive velocity exists as long as the arrival rate is below the individual Gaussian channel capacity, and provide an explicit lower bound. Our analysis involves applying pulse-amplitude modulation to the data (successively in the streaming case), and using linear mean-squared error estimation at the network nodes. For general white noise, we derive exponential error-probability bounds. For single-packet transmission over channels with (sub-)Gaussian noise, we show a doubly-exponential behavior, which reduces to the celebrated Schalkwijk–Kailath scheme when considering a single node. Viewing the constellation as an “analog source”, we also provide bounds on the exponential decay of the mean-squared error of source transmission over the network.","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"5 ","pages":"554-569"},"PeriodicalIF":0.0,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141980053","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

Neural Distributed Source Coding 神经分布式源编码

IEEE journal on selected areas in information theory Pub Date : 2024-06-14 DOI: 10.1109/JSAIT.2024.3412976

Jay Whang;Alliot Nagle;Anish Acharya;Hyeji Kim;Alexandros G. Dimakis

引用次数: 0

Secure Source Coding Resilient Against Compromised Users via an Access Structure 安全源代码编码通过访问结构抵御受攻击用户的攻击

IEEE journal on selected areas in information theory Pub Date : 2024-06-10 DOI: 10.1109/JSAIT.2024.3410235

Hassan ZivariFard;Rémi A. Chou

引用次数: 0

Information-Theoretic Tools to Understand Distributed Source Coding in Neuroscience 理解神经科学中分布式源编码的信息论工具

IEEE journal on selected areas in information theory Pub Date : 2024-06-10 DOI: 10.1109/JSAIT.2024.3409683

Ariel K. Feldman;Praveen Venkatesh;Douglas J. Weber;Pulkit Grover

引用次数: 0