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Researcher profile

Mingyue Ji

Mingyue Ji contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Information Theorymath.ITDistributed, Parallel, and Cluster ComputingArtificial IntelligenceComputer VisionInformation RetrievalMachine LearningComputational Complexityeess.SPmath.OCNetworking and Internet ArchitecturePerformance

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Published work

22 published item(s)

preprint2026arXiv

Optimal Communication and Key Rate Region for Hierarchical Secure Aggregation with User Collusion

Secure aggregation is concerned with the task of securely uploading the inputs of multiple users to an aggregation server without letting the server know the inputs beyond their summation. It finds broad applications in distributed machine learning paradigms such as federated learning (FL) where multiple clients, each having access to a proprietary dataset, periodically upload their locally trained models (abstracted as inputs) to a parameter server which then generates an aggregate (e.g., averaged) model that is sent back to the clients as an initializing point for a new round of local training. To enhance the data privacy of the clients, secure aggregation protocols are developed using techniques from cryptography to ensure that the server infers no more information of the users' inputs beyond the desired aggregated input, even if the server can collude with some users. Although laying the ground for understanding the fundamental utility-security trade-off in secure aggregation, the simple star client-server architecture cannot capture more complex network architectures used in practical systems. Motivated by hierarchical federated learning, we investigate the secure aggregation problem in a $3$-layer hierarchical network consisting of clustered users connecting to an aggregation server through an intermediate layer of relays. Besides the conventional server security which requires that the server learns nothing beyond the desired sum of inputs, relay security is also imposed so that the relays infer nothing about the users' inputs and remain oblivious. For such a hierarchical secure aggregation (HSA) problem, we characterize the optimal multifaceted trade-off between communication (in terms of user-to-relay and relay-to-server communication rates) and secret key generation efficiency (in terms of individual key and source key rates).

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Optimal Rate Region for Multi-server Secure Aggregation with User Collusion

Secure aggregation is a fundamental primitive in privacy-preserving distributed learning systems, where an aggregator aims to compute the sum of users' inputs without revealing individual data. In this paper, we study a multi-server secure aggregation problem in a two-hop network consisting of multiple aggregation servers and multiple users per server, under the presence of user collusion. Each user communicates only with its associated server, while the servers exchange messages to jointly recover the global sum. We adopt an information-theoretic security framework, allowing up to $T$ users to collude with any server. We characterize the complete optimal rate region in terms of user-to-server communication rate, server-to-server communication rate, individual key rate, and source key rate. Our main result shows that the minimum communication and individual key rates are all one symbol per input symbol, while the optimal source key rate is given by $\min\{U+V+T-2,\, UV-1\}$, where $U$ denotes the number of servers and $V$ the number of users per server. The achievability is established via a linear key construction that ensures correctness and security against colluding users, while the converse proof relies on tight entropy bounds derived from correctness and security constraints. The results reveal a fundamental tradeoff between security and key efficiency and demonstrate that the multi-server architecture can significantly reduce the required key randomness compared to single-server secure aggregation. Our findings provide a complete information-theoretic characterization of secure aggregation in multi-server systems with user collusion.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Position: Let's Develop Data Probes to Fundamentally Understand How Data Affects LLM Performance

Data is fundamental to large language models (LLMs). However, understanding of what makes certain data useful for different stages of an LLM workflow, including training, tuning, alignment, in-context learning, etc., and why, remains an open question. Current approaches rely heavily on extensive experimentation with large public datasets to obtain empirical heuristics for data filtering and dataset construction. These approaches are compute intensive and lack a principled way of understanding the essence of how specific data characteristics drive LLM behavior. In this position paper, we advocate for the need of developing systematic methodologies for generating synthetic sequences from appropriately defined random processes, with the goal that these sequences can reveal useful characteristics when they are used in one or multiple stages of the LLM workflow. We refer to such sequences as data probes. By observing LLM behavior on data probes, researchers can systematically conduct studies on how data characteristics influence model performance, generalization, and robustness. The probing sequences exhibit statistical properties that can be viewed using theoretical concepts, such as typical sets, which are generalized to describe the behaviors of LLMs. This data-probe approach provides a pathway for uncovering foundational insights into the role of data in LLM training and inference, beyond empirical heuristics.

0 citations0 reviewsNo code linkedOpen access
preprint2024arXiv

HawkRover: An Autonomous mmWave Vehicular Communication Testbed with Multi-sensor Fusion and Deep Learning

Connected and automated vehicles (CAVs) have become a transformative technology that can change our daily life. Currently, millimeter-wave (mmWave) bands are identified as the promising CAV connectivity solution. While it can provide high data rate, their realization faces many challenges such as high attenuation during mmWave signal propagation and mobility management. Existing solution has to initiate pilot signal to measure channel information, then apply signal processing to calculate the best narrow beam towards the receiver end to guarantee sufficient signal power. This process takes significant overhead and time, hence not suitable for vehicles. In this study, we propose an autonomous and low-cost testbed to collect extensive co-located mmWave signal and other sensors data such as LiDAR (Light Detection and Ranging), cameras, ultrasonic, etc, traditionally for ``automated'', to facilitate mmWave vehicular communications. Intuitively, these sensors can build a 3D map around the vehicle and signal propagation path can be estimated, eliminating iterative the process via pilot signals. This multimodal data fusion, together with AI, is expected to bring significant advances in ``connected'' research.

0 citations0 reviewsNo code linkedOpen access
preprint2024arXiv

Physics-informed Generalizable Wireless Channel Modeling with Segmentation and Deep Learning: Fundamentals, Methodologies, and Challenges

Channel modeling is fundamental in advancing wireless systems and has thus attracted considerable research focus. Recent trends have seen a growing reliance on data-driven techniques to facilitate the modeling process and yield accurate channel predictions. In this work, we first provide a concise overview of data-driven channel modeling methods, highlighting their limitations. Subsequently, we introduce the concept and advantages of physics-informed neural network (PINN)-based modeling and a summary of recent contributions in this area. Our findings demonstrate that PINN-based approaches in channel modeling exhibit promising attributes such as generalizability, interpretability, and robustness. We offer a comprehensive architecture for PINN methodology, designed to inform and inspire future model development. A case-study of our recent work on precise indoor channel prediction with semantic segmentation and deep learning is presented. The study concludes by addressing the challenges faced and suggesting potential research directions in this field.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Communication-Efficient Device Scheduling for Federated Learning Using Stochastic Optimization

Federated learning (FL) is a useful tool in distributed machine learning that utilizes users' local datasets in a privacy-preserving manner. When deploying FL in a constrained wireless environment; however, training models in a time-efficient manner can be a challenging task due to intermittent connectivity of devices, heterogeneous connection quality, and non-i.i.d. data. In this paper, we provide a novel convergence analysis of non-convex loss functions using FL on both i.i.d. and non-i.i.d. datasets with arbitrary device selection probabilities for each round. Then, using the derived convergence bound, we use stochastic optimization to develop a new client selection and power allocation algorithm that minimizes a function of the convergence bound and the average communication time under a transmit power constraint. We find an analytical solution to the minimization problem. One key feature of the algorithm is that knowledge of the channel statistics is not required and only the instantaneous channel state information needs to be known. Using the FEMNIST and CIFAR-10 datasets, we show through simulations that the communication time can be significantly decreased using our algorithm, compared to uniformly random participation.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

On the Fundamental Limits of Device-to-Device Private Caching under Uncoded Cache Placement and User Collusion

In the coded caching problem, as originally formulated by Maddah-Ali and Niesen, a server communicates via a noiseless shared broadcast link to multiple users that have local storage capability. In order for a user to decode its demanded file from the coded multicast transmission, the demands of all the users must be globally known, which may violate the privacy of the users. To overcome this privacy problem, Wan and Caire recently proposed several schemes that attain coded multicasting gain while simultaneously guarantee information theoretic privacy of the users' demands. In Device-to-Device (D2D) networks, the demand privacy problem is further exacerbated by the fact that each user is also a transmitter, which appears to be needing the knowledge of the files demanded by the remaining users in order to form its coded multicast transmission. This paper shows how to solve this seemingly infeasible problem. The main contribution of this paper is the development of novel achievable and converse bounds for D2D coded caching that are to within a constant factor of one another when privacy of the users' demands must be guaranteed even in the presence of colluding users.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

A New Design of Cache-aided Multiuser Private Information Retrieval with Uncoded Prefetching

In the problem of cache-aided multiuser private information retrieval (MuPIR), a set of $K_{\rm u}$ cache-equipped users wish to privately download a set of messages from $N$ distributed databases each holding a library of $K$ messages. The system works in two phases: {\it cache placement (prefetching) phase} in which the users fill up their cache memory, and {\it private delivery phase} in which the users' demands are revealed and they download an answer from each database so that the their desired messages can be recovered while each individual database learns nothing about the identities of the requested messages. The goal is to design the placement and the private delivery phases such that the \emph{load}, which is defined as the total number of downloaded bits normalized by the message size, is minimized given any user memory size. This paper considers the MuPIR problem with two messages, arbitrary number of users and databases where uncoded prefetching is assumed, i.e., the users directly copy some bits from the library as their cached contents. We propose a novel MuPIR scheme inspired by the Maddah-Ali and Niesen (MAN) coded caching scheme. The proposed scheme achieves lower load than any existing schemes, especially the product design (PD), and is shown to be optimal within a factor of $8$ in general and exactly optimal at very high or low memory regime.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

On Secure Distributed Linearly Separable Computation

Distributed linearly separable computation, where a user asks some distributed servers to compute a linearly separable function, was recently formulated by the same authors and aims to alleviate the bottlenecks of stragglers and communication cost in distributed computation. For this purpose, the data center assigns a subset of input datasets to each server, and each server computes some coded packets on the assigned datasets, which are then sent to the user. The user should recover the task function from the answers of a subset of servers, such the effect of stragglers could be tolerated. In this paper, we formulate a novel secure framework for this distributed linearly separable computation, where we aim to let the user only retrieve the desired task function without obtaining any other information about the input datasets, even if it receives the answers of all servers. In order to preserve the security of the input datasets, some common randomness variable independent of the datasets should be introduced into the transmission. We show that any non-secure linear-coding based computing scheme for the original distributed linearly separable computation problem, can be made secure without increasing the communication cost. Then we focus on the case where the computation cost of each server is minimum and aim to minimize the size of the randomness variable introduced in the system while achieving the optimal communication cost. We first propose an information theoretic converse bound on the randomness size. We then propose secure computing schemes based on two well-known data assignments, namely fractional repetition assignment and cyclic assignment. We then propose a computing scheme with novel assignment, which strictly outperforms the above two schemes. Some additional optimality results are also obtained.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Uncoordinated Spectrum Sharing in Millimeter Wave Networks Using Carrier Sensing

We propose using Carrier Sensing (CS) for distributed interference management in millimeter-wave (mmWave) cellular networks where spectrum is shared by multiple operators that do not coordinate among themselves. In addition, even the base station sites can be shared by the operators. We describe important challenges in using traditional CS in this setting and propose enhanced CS protocols to address these challenges. Using stochastic geometry, we develop a general framework for downlink coverage probability analysis of our shared mmWave network in the presence of CS and derive the downlink coverage probability expressions for several CS protocols. To the best of our knowledge, our work is the first to investigate and analyze (using stochastic geometry) CS for mmWave networks with spectrum and BS sites shared among non-coordinating operators. We evaluate the downlink coverage probability of our shared mmWave network using simulations as well as numerical examples based on our analysis. Our evaluations show that our proposed enhancements lead to an improvement in downlink coverage probability, compared to the downlink coverage probability with no CS, for higher values of signal-to-interference and noise ratio (SINR). Interestingly, our evaluations also reveal that for lower values of SINR, not using any CS is the best strategy in terms of the downlink coverage probability.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A Combinatorial Design for Cascaded Coded Distributed Computing on General Networks

Coding theoretic approached have been developed to significantly reduce the communication load in modern distributed computing system. In particular, coded distributed computing (CDC) introduced by Li et al. can efficiently trade computation resources to reduce the communication load in MapReduce like computing systems. For the more general cascaded CDC, Map computations are repeated at r nodes to significantly reduce the communication load among nodes tasked with computing Q Reduce functions s times. In this paper, we propose a novel low-complexity combinatorial design for cascaded CDC which 1) determines both input file and output function assignments, 2) requires significantly less number of input files and output functions, and 3) operates on heterogeneous networks where nodes have varying storage and computing capabilities. We provide an analytical characterization of the computation-communication tradeoff, from which we show the proposed scheme can outperform the state-of-the-art scheme proposed by Li et al. for the homogeneous networks. Further, when the network is heterogeneous, we show that the performance of the proposed scheme can be better than its homogeneous counterpart. In addition, the proposed scheme is optimal within a constant factor of the information theoretic converse bound while fixing the input file and the output function assignments.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A New Combinatorial Coded Design for Heterogeneous Distributed Computing

Coded Distributed Computing (CDC) introduced by Li et al. in 2015 offers an efficient approach to trade computing power to reduce the communication load in general distributed computing frameworks such as MapReduce and Spark. In particular, increasing the computation load in the Map phase by a factor of r can create coded multicasting opportunities to reduce the communication load in the Shuffle phase by the same factor. However, the CDC scheme is designed for the homogeneous settings, where the storage, computation load and communication load on the computing nodes are the same. In addition, it requires an exponentially large number of input files (data batches), reduce functions and multicasting groups relative to the number of nodes to achieve the promised gain. We address the CDC limitations by proposing a novel CDC approach based on a combinatorial design, which accommodates heterogeneous networks where nodes have varying storage and computing capabilities. In addition, the proposed approach requires an exponentially less number of input files compared to the original CDC scheme proposed by Li et al. Meanwhile, the resulting computation-communication trade-off maintains the multiplicative gain compared to conventional uncoded unicast and asymptotically achieves the optimal performance proposed by Li et al.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A New Design Framework on Device-to-Device Coded Caching with Optimal Rate and Significantly Less Subpacketizations

In this paper, we propose a new design framework on Device-to-Device (D2D) coded caching networks with optimal rate but significantly less file subpacketizations compared to that of the well-known D2D coded caching scheme proposed by Ji, Caire and Molisch (JCM). The proposed design framework is referred to as the {\em Packet Type-based (PTB) design}, where D2D users are first partitioned into multiple groups, which leads to a so-called {\em raw packet saving gain}. Then the corresponding multicasting group types and packet types are specified based on the prescribed node partition. By a careful selection of transmitters within each multicasting group, a so-called {\em further splitting ratio gain} can also be achieved. By the joint effect of the {\em raw packet saving gain} and the {\em further splitting ratio gain}, an order-wise subpacketization reduction can be achieved compared to the JCM scheme while preserving the optimal rate for large system parameter regimes. In addition, as the first time presented in the literature according to our knowledge, we find that unequal subpacketizaton is a key to achieve a subpacketization gain when the number of users is odd. As a by-product, instead of directly translating shared link caching schemes to D2D caching schemes, at least for the sake of subpackeitzations, a new design framework is indeed needed.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Cache-aided Interference Management using Hypercube Combinatorial Cache Design with Reduced Subpacketizations and Order Optimal Sum-Degrees of Freedom

We consider a cache-aided interference network which consists of a library of $N$ files, $K_T$ transmitters and $K_R$ receivers (users), each equipped with a local cache of size $M_T$ and $M_R$ files respectively, and connected via a discrete-time additive white Gaussian noise (AWGN) channel. Each receiver requests an arbitrary file from the library. The objective is to design a cache placement without knowing the receivers' requests and a communication scheme such that the sum Degrees of Freedom (sum-DoF) of the delivery is maximized. This network model with one-shot transmission was firstly investigated by Naderializadeh {\em et al.}, who proposed a scheme that achieves a one-shot sum-DoF of $\min\{\frac{M_TK_T+K_RM_R}{N}, K_R\}$, which is optimal within a constant of $2$. One of the biggest limitations of this scheme is the requirement of high subpacketization level. This paper attempts to design new algorithms to reduce the file subpacketization in such a network without hurting the sum-DoF. In particular, we propose a new approach for both prefetching and linearly coded delivery based on a combinatorial design called {\em hypercube}. The proposed approach reduces the subpacketization exponentially in terms of $K_R M/N$ and achieves the identical one-shot sum DoF when $\frac{M_TK_T+K_RM_R}{N} \leq K_R$.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Cache-aided Interference Management Using Hypercube Combinatorial Cache Designs

We consider a cache-aided interference network which consists of a library of $N$ files, $K_T$ transmitters and $K_R$ receivers (users), each equipped with a local cache of size $M_T$ and $M_R$ files respectively, and connected via a discrete-time additive white Gaussian noise channel. Each receiver requests an arbitrary file from the library. The objective is to design a cache placement without knowing the receivers' requests and a communication scheme such that the sum Degrees of Freedom (sum-DoF) of the delivery is maximized. This network model has been investigated by Naderializadeh {\em et al.}, who proposed a prefetching and a delivery schemes that achieves a sum-DoF of $\min\{\frac{M_TK_T+K_RM_R}{N}, K_R\}$. One of biggest limitations of this scheme is the requirement of high subpacketization level. This paper is the first attempt in the literature (according to our knowledge) to reduce the file subpacketization in such a network. In particular, we propose a new approach for both prefetching and linear delivery schemes based on a combinatorial design called {\em hypercube}. We show that required number of packets per file can be exponentially reduced compared to the state of the art scheme proposed by Naderializadeh {\em et al.}, or the NMA scheme. When $M_TK_T+K_RM_R \geq K_R$, the achievable one-shot sum-DoF using this approach is $\frac{M_TK_T+K_RM_R}{N}$ , which shows that 1) the one-shot sum-DoF scales linearly with the aggregate cache size in the network and 2) it is within a factor of $2$ to the information-theoretic optimum. Surprisingly, the identical and near optimal sum-DoF performance can be achieved using the hypercube approach with a much less file subpacketization.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Cache-Aided Modulation for Heterogeneous Coded Caching over a Gaussian Broadcast Channel

Coded caching is an information theoretic scheme to reduce high peak hours traffic by partially prefetching files in the users local storage during low peak hours. This paper considers heterogeneous decentralized caching systems where cache of users and content library files may have distinct sizes. The server communicates with the users through a Gaussian broadcast channel. The main contribution of this paper is a novel modulation strategy to map the multicast messages generated in the coded caching delivery phase to the symbols of a signal constellation, such that users can leverage their cached content to demodulate the desired symbols with higher reliability. For the sake of simplicity, in this paper we focus only on uncoded modulation and symbol-by-symbol error probability. However, our scheme in conjunction with multilevel coded modulation can be extended to channel coding over a larger block lengths.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Coded Elastic Computing on Machines with Heterogeneous Storage and Computation Speed

We study the optimal design of heterogeneous Coded Elastic Computing (CEC) where machines have varying computation speeds and storage. CEC introduced by Yang et al. in 2018 is a framework that mitigates the impact of elastic events, where machines can join and leave at arbitrary times. In CEC, data is distributed among machines using a Maximum Distance Separable (MDS) code such that subsets of machines can perform the desired computations. However, state-of-the-art CEC designs only operate on homogeneous networks where machines have the same speeds and storage. This may not be practical. In this work, based on an MDS storage assignment, we develop a novel computation assignment approach for heterogeneous CEC networks to minimize the overall computation time. We first consider the scenario where machines have heterogeneous computing speeds but same storage and then the scenario where both heterogeneities are present. We propose a novel combinatorial optimization formulation and solve it exactly by decomposing it into a convex optimization problem for finding the optimal computation load and a "filling problem" for finding the exact computation assignment. A low-complexity "filling algorithm" is adapted and can be completed within a number of iterations equals at most the number of available machines.

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preprint2020arXiv

FLCD: A Flexible Low Complexity Design of Coded Distributed Computing

We propose a flexible low complexity design (FLCD) of coded distributed computing (CDC) with empirical evaluation on Amazon Elastic Compute Cloud (Amazon EC2). CDC can expedite MapReduce like computation by trading increased map computations to reduce communication load and shuffle time. A main novelty of FLCD is to utilize the design freedom in defining map and reduce functions to develop asymptotic homogeneous systems to support varying intermediate values (IV) sizes under a general MapReduce framework. Compared to existing designs with constant IV sizes, FLCD offers greater flexibility in adapting to network parameters and significantly reduces the implementation complexity by requiring fewer input files and shuffle groups. The FLCD scheme is the first proposed low-complexity CDC design that can operate on a network with an arbitrary number of nodes and computation load. We perform empirical evaluations of the FLCD by executing the TeraSort algorithm on an Amazon EC2 cluster. This is the first time that theoretical predictions of the CDC shuffle time are validated by empirical evaluations. The evaluations demonstrate a 2.0 to 4.24x speedup compared to conventional uncoded MapReduce, a 12% to 52% reduction in total time, and a wider range of operating network parameters compared to existing CDC schemes.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Fundamental Limits of Decentralized Data Shuffling

Data shuffling of training data among different computing nodes (workers) has been identified as a core element to improve the statistical performance of modern large-scale machine learning algorithms. Data shuffling is often considered as one of the most significant bottlenecks in such systems due to the heavy communication load. Under a master-worker architecture (where a master has access to the entire dataset and only communication between the master and the workers is allowed) coding has been recently proved to considerably reduce the communication load. This work considers a different communication paradigm referred to as decentralized data shuffling, where workers are allowed to communicate with one another via a shared link. The decentralized data shuffling problem has two phases: workers communicate with each other during the data shuffling phase, and then workers update their stored content during the storage phase. The main challenge is to derive novel converse bounds and achievable schemes for decentralized data shuffling by considering the asymmetry of the workers' storages (i.e., workers are constrained to store different files in their storages based on the problem setting), in order to characterize the fundamental limits of this problem. For the case of uncoded storage (i.e., each worker directly stores a subset of bits of the dataset), this paper proposes converse and achievable bounds (based on distributed interference alignment and distributed clique-covering strategies) that are within a factor of 3/2 of one another. The proposed schemes are also exactly optimal under the constraint of uncoded storage for either large storage size or at most four workers in the system.

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preprint2020arXiv

Heterogeneous Computation Assignments in Coded Elastic Computing

We study the optimal design of a heterogeneous coded elastic computing (CEC) network where machines have varying relative computation speeds. CEC introduced by Yang {\it et al.} is a framework which mitigates the impact of elastic events, where machines join and leave the network. A set of data is distributed among storage constrained machines using a Maximum Distance Separable (MDS) code such that any subset of machines of a specific size can perform the desired computations. This design eliminates the need to re-distribute the data after each elastic event. In this work, we develop a process for an arbitrary heterogeneous computing network to minimize the overall computation time by defining an optimal computation load, or number of computations assigned to each machine. We then present an algorithm to define a specific computation assignment among the machines that makes use of the MDS code and meets the optimal computation load.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

On Optimal Load-Memory Tradeoff of Cache-Aided Scalar Linear Function Retrieval

Coded caching has the potential to greatly reduce network traffic by leveraging the cheap and abundant storage available in end-user devices so as to create multicast opportunities in the delivery phase. In the seminal work by Maddah-Ali and Niesen (MAN), the shared-link coded caching problem was formulated, where each user demands one file (i.e., single file retrieval). This paper generalizes the MAN problem so as to allow users to request scalar linear functions of the files. This paper proposes a novel coded delivery scheme that, based on MAN uncoded cache placement, is shown to allow for the decoding of arbitrary scalar linear functions of the files (on arbitrary finite fields). Interestingly, and quite surprisingly, it is shown that the load for cache-aided scalar linear function retrieval depends on the number of linearly independent functions that are demanded, akin to the cache-aided single-file retrieval problem where the load depends on the number of distinct file requests. The proposed scheme is optimal under the constraint of uncoded cache placement, in terms of worst-case load, and within a factor 2 otherwise. The key idea of this paper can be extended to all scenarios which the original MAN scheme has been extended to, including demand-private and/or device-to-device settings.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Topological Coded Distributed Computing

This paper considers the MapReduce-like coded distributed computing framework originally proposed by Li et al., which uses coding techniques when distributed computing servers exchange their computed intermediate values, in order to reduce the overall traffic load. Their original model servers are connected via an error-free common communication bus allowing broadcast transmissions. However, this assumption is one of the major limitations in practice since the practical cloud computing network topologies are far more involved than a simple single bus. We formulate a topological coded distributed computing problem, where the distributed servers communicate with each other through some switch network. By using a special instance of fat-tree topologies, referred to as t-ary fat-tree proposed by Al-Fares et al. which can be built by some cheap switches, we propose a coded distributed computing scheme to achieve the minimum max-link communication load defined as the maximum load over all links.

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