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

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

On the Fundamental Limit of Distributed Learning With Interchangable Constrained Statistics 论可互换约束统计分布式学习的基本极限

IEEE journal on selected areas in information theory Pub Date : 2024-06-04 DOI: 10.1109/JSAIT.2024.3409426

Xinyi Tong;Jian Xu;Shao-Lun Huang

{"title":"On the Fundamental Limit of Distributed Learning With Interchangable Constrained Statistics","authors":"Xinyi Tong;Jian Xu;Shao-Lun Huang","doi":"10.1109/JSAIT.2024.3409426","DOIUrl":"https://doi.org/10.1109/JSAIT.2024.3409426","url":null,"abstract":"In the popular federated learning scenarios, distributed nodes often represent and exchange information through functions or statistics of data, with communicative processes constrained by the dimensionality of transmitted information. This paper investigates the fundamental limits of distributed parameter estimation and model training problems under such constraints. Specifically, we assume that each node can observe a sequence of i.i.d. sampled data and communicate statistics of the observed data with dimensionality constraints. We first show the Cramer-Rao lower bound (CRLB) and the corresponding achievable estimators for the distributed parameter estimation problems, and the geometric insights and the computable algorithms of designing efficient estimators are also presented. Moreover, we consider model parameters training for distributed nodes with limited communicable statistics. We demonstrate that in order to optimize the excess risk, the feature functions of the statistics shall be designed along the largest eigenvectors of a matrix induced by the model training loss function. In summary, our results potentially provide theoretical guidelines of designing efficient algorithms for enhancing the performance of distributed learning systems.","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"5 ","pages":"396-406"},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141624095","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

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系统

IEEE journal on selected areas in information theory Pub Date : 2024-04-29 DOI: 10.1109/JSAIT.2024.3391849

Baturalp Buyukates;Jinhyun So;Hessam Mahdavifar;Salman Avestimehr

{"title":"LightVeriFL: A Lightweight and Verifiable Secure Aggregation for Federated Learning","authors":"Baturalp Buyukates;Jinhyun So;Hessam Mahdavifar;Salman Avestimehr","doi":"10.1109/JSAIT.2024.3391849","DOIUrl":"https://doi.org/10.1109/JSAIT.2024.3391849","url":null,"abstract":"Secure aggregation protects the local models of the users in federated learning, by not allowing the server to obtain any information beyond the aggregate model at each iteration. Naively implementing secure aggregation fails to protect the integrity of the aggregate model in the possible presence of a malicious server forging the aggregation result, which motivates verifiable aggregation in federated learning. Existing verifiable aggregation schemes either have a linear complexity in model size or require time-consuming reconstruction at the server, that is quadratic in the number of users, in case of likely user dropouts. To overcome these limitations, we propose \u0000<monospace>LightVeriFL</monospace>\u0000, a lightweight and communication-efficient secure verifiable aggregation protocol, that provides the same guarantees for verifiability against a malicious server, data privacy, and dropout-resilience as the state-of-the-art protocols without incurring substantial communication and computation overheads. The proposed \u0000<monospace>LightVeriFL</monospace>\u0000 protocol utilizes homomorphic hash and commitment functions of constant length, that are independent of the model size, to enable verification at the users. In case of dropouts, \u0000<monospace>LightVeriFL</monospace>\u0000 uses a one-shot aggregate hash recovery of the dropped-out users, instead of a one-by-one recovery, making the verification process significantly faster than the existing approaches. Comprehensive experiments show the advantage of \u0000<monospace>LightVeriFL</monospace>\u0000 in practical settings.","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"5 ","pages":"285-301"},"PeriodicalIF":0.0,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141096202","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

Learning Algorithm Generalization Error Bounds via Auxiliary Distributions 通过辅助分布实现学习算法泛化误差边界

IEEE journal on selected areas in information theory Pub Date : 2024-04-25 DOI: 10.1109/JSAIT.2024.3391900

Gholamali Aminian;Saeed Masiha;Laura Toni;Miguel R. D. Rodrigues

{"title":"Learning Algorithm Generalization Error Bounds via Auxiliary Distributions","authors":"Gholamali Aminian;Saeed Masiha;Laura Toni;Miguel R. D. Rodrigues","doi":"10.1109/JSAIT.2024.3391900","DOIUrl":"https://doi.org/10.1109/JSAIT.2024.3391900","url":null,"abstract":"Generalization error bounds are essential for comprehending how well machine learning models work. In this work, we suggest a novel method, i.e., the Auxiliary Distribution Method, that leads to new upper bounds on expected generalization errors that are appropriate for supervised learning scenarios. We show that our general upper bounds can be specialized under some conditions to new bounds involving the \u0000<inline-formula> <tex-math>$alpha $ </tex-math></inline-formula>\u0000-Jensen-Shannon, \u0000<inline-formula> <tex-math>$alpha $ </tex-math></inline-formula>\u0000-Rényi \u0000<inline-formula> <tex-math>$(0lt alpha lt 1)$ </tex-math></inline-formula>\u0000 information between a random variable modeling the set of training samples and another random variable modeling the set of hypotheses. Our upper bounds based on \u0000<inline-formula> <tex-math>$alpha $ </tex-math></inline-formula>\u0000-Jensen-Shannon information are also finite. Additionally, we demonstrate how our auxiliary distribution method can be used to derive the upper bounds on excess risk of some learning algorithms in the supervised learning context and the generalization error under the distribution mismatch scenario in supervised learning algorithms, where the distribution mismatch is modeled as \u0000<inline-formula> <tex-math>$alpha $ </tex-math></inline-formula>\u0000-Jensen-Shannon or \u0000<inline-formula> <tex-math>$alpha $ </tex-math></inline-formula>\u0000-Rényi divergence between the distribution of test and training data samples distributions. We also outline the conditions for which our proposed upper bounds might be tighter than other earlier upper bounds.","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"5 ","pages":"273-284"},"PeriodicalIF":0.0,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141096301","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 Compressor Discovers Binning 神经分布式压缩器发现分选功能

IEEE journal on selected areas in information theory Pub Date : 2024-04-24 DOI: 10.1109/JSAIT.2024.3393429

Ezgi Ozyilkan;Johannes Ballé;Elza Erkip

引用次数: 0

Training Generative Models From Privatized Data via Entropic Optimal Transport 通过熵优化传输从私有化数据中训练生成模型

IEEE journal on selected areas in information theory Pub Date : 2024-04-16 DOI: 10.1109/JSAIT.2024.3387463

Daria Reshetova;Wei-Ning Chen;Ayfer Özgür

引用次数: 0

Differentially Private Stochastic Linear Bandits: (Almost) for Free 差分私有随机线性强盗：（几乎）免费

IEEE journal on selected areas in information theory Pub Date : 2024-04-16 DOI: 10.1109/JSAIT.2024.3389954

Osama Hanna;Antonious M. Girgis;Christina Fragouli;Suhas Diggavi

{"title":"Differentially Private Stochastic Linear Bandits: (Almost) for Free","authors":"Osama Hanna;Antonious M. Girgis;Christina Fragouli;Suhas Diggavi","doi":"10.1109/JSAIT.2024.3389954","DOIUrl":"https://doi.org/10.1109/JSAIT.2024.3389954","url":null,"abstract":"In this paper, we propose differentially private algorithms for the problem of stochastic linear bandits in the central, local and shuffled models. In the central model, we achieve almost the same regret as the optimal non-private algorithms, which means we get privacy for free. In particular, we achieve a regret of \u0000<inline-formula> <tex-math>$tilde {O}left({sqrt {T}+{}frac {1}{varepsilon }}right)$ </tex-math></inline-formula>\u0000 matching the known lower bound for private linear bandits, while the best previously known algorithm achieves \u0000<inline-formula> <tex-math>$tilde {O}left({{}frac {1}{varepsilon }sqrt {T}}right)$ </tex-math></inline-formula>\u0000. In the local case, we achieve a regret of \u0000<inline-formula> <tex-math>$tilde {O}left({{}frac {1}{varepsilon }{sqrt {T}}}right)$ </tex-math></inline-formula>\u0000 which matches the non-private regret for constant \u0000<inline-formula> <tex-math>$varepsilon $ </tex-math></inline-formula>\u0000, but suffers a regret penalty when \u0000<inline-formula> <tex-math>$varepsilon $ </tex-math></inline-formula>\u0000 is small. In the shuffled model, we also achieve regret of \u0000<inline-formula> <tex-math>$tilde {O}left({sqrt {T}+{}frac {1}{varepsilon }}right)$ </tex-math></inline-formula>\u0000 while the best previously known algorithm suffers a regret of \u0000<inline-formula> <tex-math>$tilde {O}left({{}frac {1}{varepsilon }{T^{3/5}}}right)$ </tex-math></inline-formula>\u0000. Our numerical evaluation validates our theoretical results. Our results generalize for contextual linear bandits with known context distributions.","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"5 ","pages":"135-147"},"PeriodicalIF":0.0,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140818731","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