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Edge computing-Aided coded Vertical Federated Linear Regression

IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2022年第3期8卷 1543-1551页

作者： Dai, Mingjun Zheng, Ziying Hong, Zhaoyan Zhang, Shengli Wang, Hui Shenzhen Univ Coll Elect & Informat Engn Guangdong Prov Engn Ctr Ubiquitous Comp & Intelli Shenzhen 518060 Peoples R China Shenzhen Univ Coll Math & Stat Shenzhen 518060 Peoples R China Shenzhen Inst Informat Technol Commun Engn Dept Shenzhen 518109 Peoples R China

For the training process of federated linear regression (FLR), which is the simplest form of federated learning, the integrated computation at each company is slowed down either by huge volume data or by time-consuming homomorphic encryption. Targetted at accelerating the training process of FLR, through the incorporation of edge computing aided coded distributed computing (CDC) into intensive computation (matrix multiplication), a novel coded FLR framework is proposed where several edge nodes aid the computing of one company. Two schemes, including linear combination (LC)-based vertical FLR and Matdot-based vertical FLR, are proposed and designed, which enjoy in-parallel computation and homomorphic encryption at the edge nodes. Since workload at each edge node is reduced significantly, the training runtime of these two schemes may be reduced significantly. Numerical studies show that our proposed coded schemes outperform traditional uncoded schemes significantly in terms of overall runtime (sum of encoding, computing, and decoding phases) of the training process. Besides, among the two proposed coded schemes, LC-based scheme and Matdot-based scheme each has its own advantage scenarios which conforms with the analysis.

关键词： Vertical federated learning federated linear regression coded distributed computing edge computing

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Multi-Access distributed computing

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IEEE TRANSACTIONS ON INFORMATION THEORY 2024年第5期70卷 3385-3398页

作者： Brunero, Federico Elia, Petros EURECOM Commun Syst Dept F-06410 Sophia Antipolis France Huawei Munich Res Ctr Opt & Quantum Commun Lab D-80992 Munich Germany

coded distributed computing (CDC) is a new technique proposed with the purpose of decreasing the intense data exchange required for parallelizing distributed computing systems. Under the famous MapReduce paradigm, this coded approach has been shown to decrease this communication overhead by a factor that is linearly proportional to the overall computation load during the mapping phase. In this paper, we propose multi-access distributed computing (MADC) as a generalization of the original CDC model, where now mappers (nodes in charge of the map functions) and reducers (nodes in charge of the reduce functions) are distinct computing nodes that are connected through a multi-access network topology. Focusing on the MADC setting with combinatorial topology, which implies Lambda mappers and K reducers such that there is a unique reducer connected to any alpha mappers, we propose a coded scheme and an information-theoretic converse, which jointly identify the optimal inter-reducer communication load, as a function of the computation load, to within a constant gap of 1.5. Additionally, a modified coded scheme and converse identify the optimal max-link communication load across all existing links to within a gap of 4.

关键词： coded distributed computing coded multicasting communication load information-theoretic converse MapReduce communication complexity multi-access distributed computing (MADC)

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Secure and Efficient coded Multi-Access Edge computing With Generalized Graph Neural Networks

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IEEE TRANSACTIONS ON MOBILE computing 2023年第9期22卷 5504-5524页

作者： Asheralieva, Alia Niyato, Dusit Southern Univ Sci & Technol Res Inst Trustworthy Autonomous Syst Dept Comp Sci & Engn Shenzhen 518055 Guangdong Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore

We formulate a novel framework to improve security and utility of the coded multi-access edge computing (MEC) network for Internet of Things (IoT) applications where multiple edge servers (ESs) jointly process raw IoT data to obtain the final network output. To correctly recover the final output even when some processing outputs produced by malicious or malfunctioning ESs are erroneous, the network utilizes coded distributed computing (CDC) that enhances security by adding computational redundancy to the data processed by ESs. Within the framework, we propose an advanced approach to address limitations of contemporary CDC-based systems related to their inability to guarantee security when the number of malicious ESs is large and reduced network utility due to redundant computations. In this approach, the processing loads are allocated to ESs based on deep learning (DL) algorithms to identify the unknown ESs' types (faithful or malicious) and minimize the load of malicious ESs, thereby optimizing security and utility. The proposed DL algorithms adopt the message passing neural network (NN) - a generalized graph NN with lower complexity and faster convergence than conventional NNs. We prove that our framework yields the optimal security and utility, and verify its superior performance compared with the state-of-the-art schemes.

关键词： coded distributed computing computational offloading deep learning graph neural networks Internet of Things Lagrange inter-polation machine learning multi-access edge computing resource allocation security

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A Hierarchical Incentive Design Toward Motivating Participation in coded Federated Learning

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2022年第1期40卷 359-375页

作者： Ng, Jer Shyuan Lim, Wei Yang Bryan Xiong, Zehui Cao, Xianbin Niyato, Dusit Leung, Cyril Kim, Dong In Alibaba Grp Hangzhou 311121 Peoples R China Nanyang Technol Univ NTU Alibaba NTU Joint Res Inst Singapore 639798 Singapore Singapore Univ Technol & Design SUTD Pillar Informat Syst Technol & Design Singapore 487372 Singapore Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Nanyang Technol Univ NTU Sch Comp Sci & Engn Singapore 639798 Singapore Univ British Columbia UBC Dept Elect & Comp Engn Vancouver BC V6T 1Z4 Canada Joint NTU UBC Res Ctr Excellence Act Living Elder Singapore 639798 Singapore Sungkyunkwan Univ SKKU Dept Elect & Comp Engn Seoul 03063 South Korea

Federated Learning (FL) is a privacy-preserving collaborative learning approach that trains artificial intelligence (AI) models without revealing local datasets of the FL workers. While FL ensures the privacy of the FL workers, its performance is limited by several bottlenecks, which become significant given the increasing amounts of data generated and the size of the FL network. One of the main challenges is the straggler effects where the significant computation delays are caused by the slow FL workers. As such, coded Federated Learning (CFL), which leverages coding techniques to introduce redundant computations to the FL server, has been proposed to reduce the computation latency. In CFL, the FL server helps to compute a subset of the partial gradients based on the composite parity data and aggregates the computed partial gradients with those received from the FL workers. In order to implement the coding schemes over the FL network, incentive mechanisms are important to allocate the resources of the FL workers and data owners efficiently in order to complete the CFL training tasks. In this paper, we consider a two-level incentive mechanism design problem. In the lower level, the data owners are allowed to support the FL training tasks of the FL workers by contributing their data. To model the dynamics of the selection of FL workers by the data owners, an evolutionary game is adopted to achieve an equilibrium solution. In the upper level, a deep learning based auction is proposed to model the competition among the model owners.

关键词： Computational modeling Data models Training Servers Task analysis Statistics Sociology coded distributed computing federated Learning straggler effects evolutionary deep learning auction

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Fast and Secure Computational Offloading With Lagrange coded Mobile Edge computing

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2021年第5期70卷 4924-4942页

作者： Asheralieva, Alia Niyato, Dusit Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Guangdong Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore

This paper proposes a novel framework based on Lagrange coded computing (LCC) for fast and secure offloading of computing tasks in the mobile edge computing (MEC) network. The network is formed by multiple base stations (BSs) acting as "masters" which offload their computations to edge devices acting as "workers". The framework aims to ensure efficient allocation of computing loads and bandwidths to workers, and providing them with proper incentives to finish their tasks by the specified deadlines. Thus, each master must decide on the amounts of allocated load and bandwidth, and a service fee paid to each worker given that: i) other masters, i.e., BSs, can be privately-owned or controlled by different operators, i.e., they do not communicate/coordinate their decisions with the master;ii) workers are heterogeneous non-dedicated edge devices with constrained and nondeterministic computing resources. As such, masters compete for the best workers in a stochastic and partially-observable environment. To describe interactions between masters and workers, we formulate a new stochastic auction model with contingent values of bidders, i.e., masters and contingent payments to auctioneers, i.e., workers. To solve the auction, we represent it as a stochastic Bayesian game and develop machine learning algorithms to improve the auction solution.

关键词： Task analysis Stochastic processes distributed computing Resilience Servers Security Resource management Auction theory Bayesian methods coded distributed computing game theory incomplete information Internet of things machine learning Markov processes mobile edge computing resiliency resource allocation and pricing security

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codedPaddedFL and codedSecAgg: Straggler Mitigation and Secure Aggregation in Federated Learning

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IEEE TRANSACTIONS ON COMMUNICATIONS 2023年第4期71卷 2013-2027页

作者： Schlegel, Reent Kumar, Siddhartha Rosnes, Eirik Graell i Amat, Alexandre Simula UiB N-5006 Bergen Norway OHB Digital Connect GmbH D-28359 Bremen Germany Qamcom Res & Technol S-41285 Gothenburg Sweden Chalmers Univ Technol Dept Elect Engn S-41296 Gothenburg Sweden

We present two novel federated learning (FL) schemes that mitigate the effect of straggling devices by introducing redundancy on the devices' data across the network. Compared to other schemes in the literature, which deal with stragglers or device dropouts by ignoring their contribution, the proposed schemes do not suffer from the client drift problem. The first scheme, codedPaddedFL, mitigates the effect of stragglers while retaining the privacy level of conventional FL. It combines one-time padding for user data privacy with gradient codes to yield straggler resiliency. The second scheme, codedSecAgg, provides straggler resiliency and robustness against model inversion attacks and is based on Shamir's secret sharing. We apply codedPaddedFL and codedSecAgg to a classification problem. For a scenario with 120 devices, codedPaddedFL achieves a speed-up factor of 18 for an accuracy of 95% on the MNIST dataset compared to conventional FL. Furthermore, it yields similar performance in terms of latency compared to a recently proposed scheme by Prakash et al. without the shortcoming of additional leakage of private data. codedSecAgg outperforms the state-of-the-art secure aggregation scheme LightSecAgg by a speed-up factor of 6.6-18.7 for the MNIST dataset for an accuracy of 95%.

关键词： Servers Codes Resilience Data models Training Decoding Data privacy coded distributed computing federated learning gradient codes linear regression privacy secure aggregation straggler mitigation

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Auction-and-Learning Based Lagrange coded computing Model for Privacy-Preserving, Secure, and Resilient Mobile Edge computing

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IEEE TRANSACTIONS ON MOBILE computing 2023年第2期22卷 744-764页

作者： Asheralieva, Alia Niyato, Dusit Xiong, Zehui Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Guangdong Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore Singapore Univ Technol & Design Informat Syst Technol & Design Pillar Singapore 487372 Singapore

We design a novel encoding model based on Lagrange coded computing (LCC) for private, secure, and resilient distributed mobile edge computing (MEC) systems, where multiple base stations (BSs) act as "masters" offloading their computations to edge nodes acting as "workers". A two-fold objective of the scheme is: i) efficient allocation of computing tasks to the workers;ii) providing the workers with appropriate incentives to complete their tasks. As such, each master must decide on its offloading requests to the workers including the allocated tasks and service fees to be paid. This problem is complex due to the following reasons: i) masters can be privately-owned or managed by different operators, i.e., there is no communication and no coordination among them;ii) workers are heterogeneous non-dedicated nodes with limited and nondeterministic transmission and computing resources. As a result, the masters must compete for constrained resources of workers in a stochastic partially-observable environment. To address this problem, we define the interactions between masters and workers as a direct stochastic first-price-sealed-bid (FPSB) auction. To analyze the auction, we represent it as a stochastic Bayesian game and develop a Bayesian learning framework to perfect the auction solution.

关键词： Task analysis distributed computing Computational modeling Security Resilience Bayes methods Privacy Auction theory Bayesian methods coded distributed computing data privacy deep learning game theory incomplete information Internet of Things machine learning Markov processes mobile edge computing pricing reinforcement learning resiliency resource allocation security

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A Systematic Approach Towards Efficient Private Matrix Multiplication

IEEE JOURNAL ON SELECTED AREAS IN INFORMATION THEORY

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IEEE JOURNAL ON SELECTED AREAS IN INFORMATION THEORY 2022年第2期3卷 257-274页

作者： Zhu, Jinbao Li, Songze Hong Kong Univ Sci & Technol Guangzhou Thrust Internet Things Guangzhou 510006 Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Peoples R China

We consider the problems of Private and Secure Matrix Multiplication (PSMM) and Fully Private Matrix Multiplication (FPMM), for which matrices privately selected by a master node are multiplied at distributed worker nodes without revealing the indices of the selected matrices, even when a certain number of workers collude with each other. We propose a novel systematic approach to solve PSMM and FPMM with colluding workers, which leverages solutions to a related Secure Matrix Multiplication (SMM) problem where the data (rather than the indices) of the multiplied matrices are kept private from colluding workers. Specifically, given an SMM strategy based on polynomial codes or Lagrange codes, one can exploit the special structure inspired by the matrix encoding function to design private coded queries for PSMM/FPMM, such that the algebraic structure of the computation result at each worker resembles that of the underlying SMM strategy. Adopting this systematic approach provides novel insights in private query designs for private matrix multiplication, substantially simplifying the processes of designing PSMM and FPMM strategies. Furthermore, the PSMM and FPMM strategies constructed following the proposed approach outperform the state-of-the-art strategies in one or more performance metrics including recovery threshold (minimal number of workers the master needs to wait for before correctly recovering the multiplication result), communication cost, and computation complexity, demonstrating a more flexible tradeoff in optimizing system efficiency.

关键词： coded distributed computing secure matrix multiplication private and secure matrix multiplication fully private matrix multiplication polynomial codes Lagrange codes

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Heterogeneity Shifts the Storage-Computation Tradeoff in Secure Multi-Cloud Systems

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IEEE TRANSACTIONS ON INFORMATION THEORY 2023年第2期69卷 1015-1036页

作者： Chen, Jiajun Sung, Chi Wan Chan, Terence H. City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China Univ South Australia Inst Telecommun Res Adelaide SA 5095 Australia

This paper considers the design of heterogeneous multi-cloud systems for big data storage and computing in the presence of cloud collusion and failures. A fundamental concept of such a system is the secrecy capacity, which represents the maximum amount of information that can be stored for each unit of storage space under the requirements of secure distributed computing. A capacity-achieving code is designed for matrix multiplication, a computing subroutine widely used in machine learning applications. The code allows fast parallel decoding and unequal data allocation in the clouds. Such a flexibility leads naturally to the idea of optimizing data allocation to minimize the computing time. Given any feasible storage budget, the optimal solution is derived, characterizing explicitly the fundamental tradeoff between storage and computing. Furthermore, it is shown via majorization theory that the whole tradeoff curve improves if the cloud computing rates are more even. Experiments on Amazon EC2 clusters are conducted, corroborating our theoretical observations and the negligibility of decoding overhead.

关键词： Cloud computing Codes Servers Decoding Resource management Runtime Security coded distributed computing heterogeneous systems multi-cloud computing secrecy capacity storage-computation tradeoff

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Deadline-Aware coded Computation Across Homogeneous Workers

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IEEE SIGNAL PROCESSING LETTERS 2023年 30卷 982-986页

作者： Mehrabi, Mehrad Ardakani, Maryam Haghighi Ardakani, Masoud Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2R3 Canada

distributed computing systems have been widely used in recent years to handle massive computations required by newly emerged machine learning algorithms and signal processing problems. In practice, a distributed computing system often receives multiple tasks each needs to be finished by a specific deadline. This necessitates use of a task scheduler which orders and prioritizes tasks executions. In this work, we consider task scheduling for a homogeneous distributed computing system with multiple matrix-vector multiplication jobs, and try to maximize the number of tasks completed before their deadlines. The main challenges in such a system are random task arrivals and random execution times due to the straggling effect. To address these challenges, we propose two task scheduling algorithms namely "simple greedy" and "farsighted greedy" and compare their performance with the ultimate upper bound, i.e., a genie-aided algorithm that knows the exact arrival and execution times of all tasks. Our simulation results demonstrate that the proposed algorithms can approach the performance of the genie-aided algorithm.

关键词： coded distributed computing task scheduling random task arrival task allocation coding rate

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