检索结果-内蒙古大学图书馆

Privacy-Preserving Coded Schemes for Multi-Server Federated Learning With Straggling Links

IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2025年 20卷 1222-1236页

作者： Liang, Kai Li, Songze Ding, Ming Tian, Feng Wu, Youlong ShanghaiTech Univ Sch Informat Sci & Technol Shanghai 201210 Peoples R China Southeast Univ Minist Educ Sch Cyber Sci & Engn Nanjing 210003 Peoples R China Southeast Univ Minist Educ Engn Res Ctr Blockchain Applicat Supervis & Manag Nanjing 210003 Peoples R China CSIRO Data61 Sydney NSW 2015 Australia Chinese Acad Sci Innovat Acad Microsatellites Shanghai 201210 Peoples R China

Federated Learning (FL) has emerged as an unparalleled machine learning paradigm where multiple edge clients jointly train a global model without sharing the raw data. However, sharing local models or gradients still compromises clients' privacy and could be susceptible to delivery failures due to unreliable communication links. To address these issues, this paper considers a multi-server FL where E edge clients wish to jointly train the global model with the help of H servers while guaranteeing data privacy and meanwhile combating s <= H unreliable links per client. We first propose a hybrid coding scheme based on repetition coding and MDS coding, such that any T-s colluding servers cannot deduce any client data besides the aggregated model, and any T-e colluding clients remain unaware of honest clients' data. Furthermore, we propose a Lagrange coding with mask (LCM) to ensure more stringent privacy protection that additionally demands that colluding servers possess no knowledge about either the local or global models. Furthermore, we establish lower bounds for both the uplink and downlink communication loads and theoretically prove that the hybrid scheme and LCM scheme can achieve the optimal uplink communication loads under the first and second threat models, respectively. For the second threat model with no straggling link, the LCM scheme is optimal. These demonstrate the communication efficiency, robustness, and privacy guarantee of our schemes.

关键词： coding computing distributed learning straggling links secure aggregation

来源：评论

学校读者我要写书评

暂无评论

Two-layer Coded Gradient Aggregation with Straggling Communication Links

Two-layer Coded Gradient Aggregation with Straggling Communi...

引用

IEEE Information Theory Workshop (ITW)

作者： Liang, Kai Wu, Youlong ShanghaiTech Univ Shanghai Peoples R China Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781728159621

In many distributed learning setups such as federated learning, client nodes at the edge use individually collected data to compute the local gradients and send them to a central master server, and the master aggregates the received gradients and broadcasts the aggregation to all clients with which the clients can update the global model. As straggling communication links could severely affect the performance of distributed learning system, Prakash et al. proposed to utilize helper nodes and coding strategy to achieve resiliency against straggling client-to-helpers links. In this paper, we propose two coding schemes: repetition coding (RC) and MDS coding both of which enable the clients to update the global model in the presence of only helpers but without the master. Moreover, we characterize the uplink and downlink communication loads, and prove the tightness of uplink communication load. Theoretical tradeoff between uplink and downlink communication loads is established indicating that larger uplink communication load could reduce downlink communication load. Compared to Prakash's schemes which require a master to connect with helpers though noiseless links, our scheme can even reduce the communication load in the absence of master when the number of clients and helpers is relatively large compared to the number of straggling links.

关键词： coding computing distributed learning straggling

来源：评论

学校读者我要写书评

暂无评论

Multi-Server Secure Aggregation with Unreliable Communication Links

Multi-Server Secure Aggregation with Unreliable Communicatio...

引用

IEEE Conference on Global Communications (IEEE GLOBECOM) - Intelligent Communications for Shared Prosperity

作者： Liang, Kai Li, Songze Ding, Ming Wu, Youlong ShanghaiTech Univ Shanghai Peoples R China Hong Kong Univ Sci & Technol Guangzhou Guangzhou Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China CSIRO Data61 Sydney NSW 2015 Australia

ISBN: (纸本)9798350310900

Federated learning (FL) is a novel training paradigm that allows clients to jointly train models locally without sharing their data. However, it encounters various challenges, such as limited client communication resources, unreliable client-server connections, and the privacy leakage of client information. In this paper, we consider multi-server secure FL with unreliable communication links. We first define a threat model using Shannon's information-theoretic security framework, and propose a novel scheme called Lagrange coding with Mask (LCM), which introduces client-side masking to reduce the disclosure of client information and injects appropriate coding redundancy to counter the effects of unreliable links while ensuring security. Furthermore, we derive the lower bounds of the uplink and downlink communication loads, respectively, and prove that LCM achieves the optimal uplink communication load, which is unrelated to the number of collusion clients.

关键词： coding computing federated learning straggling links secure aggregation

来源：评论

学校读者我要写书评

暂无评论

Research on distributed coding computation based on matrix multiplication

Research on distributed coding computation based on matrix m...

引用

作者： Lin Zhang Xi'an University of Technology

Matrix multiplication is used in machine learning to train larger,more accurate models with massive data more *** distributed machine learning paradigm necessitates the distributed computing of many matrix multiplications once it is *** execution time of distributed machine learning algorithms increases on dropout nodes,which are computational nodes in distributed clusters that randomly slow down computation due to a variety of factors(e.g.,node failure,system failure,communication bottlenecks,etc.),becoming a significant bottleneck in distributed computing *** has been discovered that coded computation is less expensive than replica methods and can more effectively reduce the effects of dropped *** this study,we present theoretical insights on how encoded solutions might produce significant gains over unencoded solutions.

关键词： coding computing distributed machine learning matrix multiplication

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：