Distributed computing has emerged as a promising solution for accelerating machine learning training processes on large-scale datasets by leveraging the parallel processing capabilities of multiple workers. However, there remain two major issues that still need to be addressed: i) Byzantine attacks from malicious workers, and ii) the effect of slow workers, commonly referred to as stragglers. In this paper, we address both issues concurrently by introducing Group-wise Verifiable Coded Computing (GVCC), a novel approach that combines coding techniques and group-wise verification to enhance robustness against Byzantine attacks and resilience to straggler effects in distributed computing. The key idea of GVCC is to verify a group of computation results from workers at a time, while providing resilience to stragglers through encoding tasks assigned to workers with Group-wise Verifiable Codes. We evaluate the performance of GVCC through experiments conducted on Amazon EC2 clouds and the results show that GVCC outperforms the existing methods in terms of overall processing time and verification time while maintaining the verification performance. This study highlights the potential of GVCC as an effective solution for overcoming the challenges of Byzantine attacks and stragglers in distributed computing for executing matrix multiplication.

In this paper, we consider coded computation for matrix multiplication tasks in distributed computing to mitigate straggler effects. We assume that the stragglers’ computation results can be leveraged at the master by assigning multiple sub-tasks to the workers. We propose a new coded computation scheme, namely Chebyshev coded fully private matrix multiplication (CFP), to preserve the privacy of a master in a scenario where a master wants to obtain a matrix multiplication result from the libraries which are shared by the workers, while concealing both of the two indices of the desired matrices from each worker. The key idea of CFP is to introduce Chebyshev polynomials, which have commutative property, in queries sent to workers to allocate sub-tasks. We also extend CFP to keep the privacy of a master from colluding workers. In conclusion, we show that CFP can preserve the privacy of a master from each worker and efficiently mitigate straggler effects compared to existing schemes.

Coded computing has proved its efficiency in handling a straggler issue in distributed computing framework. However, in a coded distributed computing framework, there may exist Byzantine workers who send the wrong computation results to a master to contaminate the overall computation output. Therefore, it is essential to identify Byzantine workers from their computation results in coded computing. In this paper, we consider Byzantine attack identification problem in coded computing for distributed matrix multiplication tasks. We propose locally testable codes which facilitate the efficient Byzantine attack identification, and suggest a hierarchical group testing method for Byzantine attack identification. We show that our scheme requires smaller number of tests than the conventional group testing methods for the existing coded computing schemes.

Coded computing has proved its efficiency in handling a straggler issue in distributed computing framework. It uses error correcting codes to mitigate the effect of the stragglers. However, in a coded distributed computing framework, there may exist Byzantine workers who send the wrong computation results to a master in order to contaminate the overall computation output. Therefore, it is essential to identify Byzantine workers from their computation results in coded computing. In this paper, we consider Byzantine attack identification problem in coded computing for distributed matrix multiplication tasks. We propose a new coding scheme which facilitates the efficient Byzantine attack identification, namely locally testable codes. We also suggest a hierarchical group testing method for Byzantine attack identification. We claim the required number of tests for group testing in our scheme, and show that it requires smaller number of tests than the conventional group testing method for the existing coded computing schemes.

Distributed computing has been a prominent solution to efficiently process massive datasets in parallel. However, the existence of stragglers is one of the major concerns that slows down the overall speed of distributed computing. To deal with this problem, we consider a distributed matrix multiplication scenario where a master assigns multiple tasks to each worker to exploit stragglers’ computing ability (which is typically wasted in conventional distributed computing). We propose Chebyshev polynomial codes, which can achieve order-wise improvement in encoding complexity at the master and communication load in distributed matrix multiplication using task entanglement. The key idea of task entanglement is to reduce the number of encoded matrices for multiple tasks assigned to each worker by intertwining encoded matrices. We experimentally demonstrate that, in cloud environments, Chebyshev polynomial codes can provide significant reduction in overall processing time in distributed computing for matrix multiplication, which is a key computational component in modern deep learning.

In this paper, we consider coded computation for matrix multiplication tasks in distributed computing, which can mitigate the effect of slow workers, called stragglers, by a coding approach. We assume that the stragglers' computation results can be leveraged at the master by assigning multiple sub-tasks to the workers. In this scenario, we propose a new coded computation scheme to preserve the data privacy and security from the non-colluding workers. We also prove that the data privacy and security constraints are satisfied in our scheme in an information-theoretic sense.

In distributed computing system for the master-worker framework, an erasure code is able to mitigate the effects of slow workers, also called stragglers. The distributed computing system combined with coding is referred to as coded computation. For a matrix multiplication, we consider a variation of coded computation that ensures the master's privacy from the workers, which is referred to as private coded matrix multiplication. In the private coded matrix multiplication, the master needs to compute a matrix multiplication on its own matrix and one of the matrices in a library exclusively shared by the external workers. After the master recovers the matrix multiplication through coded matrix multiplication, the workers should not know which matrix in the library was desired by the master, which implies that the master's privacy is ensured. Our problem is a special case of linear private computation, where a linear combination of matrices in the library should be concealed. We propose a private coded matrix multiplication scheme, based on the conventional coded matrix multiplication scheme. In terms of computation time and communication load, we compare our proposed scheme with a conventional robust private information retrieval scheme and private computation schemes.

In this paper, we consider a power allocation optimization technique for a time-varying fading broadcast channel in energy harvesting communication systems, in which a transmitter with a rechargeable battery transmits messages to receivers using the harvested energy. We first prove that the optimal online power allocation policy for the sum rate maximization of the transmitter is an increasing function of harvested energy, remaining battery, and each user's channel gain. We then construct an appropriate neural network by relying on increasing behavior of the optimal policy. This two-step approach, by using an effective function approximation as well as providing a fundamental guideline for neural network design, can prevent us from wasting the representational capacity of neural networks. On the basis of the neural network, we apply the policy gradient method to solve the power allocation problem. To validate the performance of our approach, we compare it with the closed-form the optimal policy in a partially observable Markov problem. Through further experiments, it is observed that our online solution achieves a performance close to the theoretical upper bound of the performance in a time-varying fading broadcast channel.

In this paper, we consider a secure distributed computing scenario in which a master wants to perform matrix multiplication of confidential inputs with multiple workers in parallel. In such a setting, a master does not want to reveal information about the two input matrices to the workers in an information-theoretic sense. We propose a secure distributed computing scheme that can efficiently cope with straggling effects by applying polynomial codes on sub-tasks assigned to workers. The achievable recovery threshold, i.e., the number of workers that a master needs to wait for to get the final product, of our proposed scheme is revealed to be order-optimal to the number of workers. Moreover, we derive the achievable recovery threshold of the proposed scheme is within a constant multiplicative factor from information-theoretic lower bound. As a byproduct, we extend our strategy to secure distributed computing for convolution tasks on confidential data.

In this paper, we consider a power allocation problem for energy harvesting communication systems, where a transmitter wants to send the desired messages to the receiver with the harvested energy in its rechargeable battery. We propose a new power allocation strategy based on deep reinforcement learning technique to maximize the expected total transmitted data for a given random energy arrival and random channel process. The key idea of our scheme is to lead the transmitter, rather than learning the undesirable power allocation policies, by an action-bounding technique using only causal knowledge of the energy and channel processes. This technique helps traditional reinforcement learning algorithms to work more accurately in the systems, and increases the performance of the learning algorithms. Moreover, we show that the proposed scheme achieves better performance with respect to the expected total transmitted data compared to existing power allocation strategies.

In this paper, we investigate a private information retrieval (PIR) problem for secure distributed storage systems in the presence of an eavesdropper. We design the secure distributed database and the corresponding PIR scheme, which protect not only user privacy (concealing the index of the desired message) from the databases, but also data security (concealing the messages themselves) from an eavesdropper. In our proposed scheme, we use a secret sharing scheme in storing the messages for data security at each of the databases. We consider two different scenarios on whether the databases are aware of the index sets of the secret shares stored in other databases. The key idea in designing an efficient PIR procedure is to exploit the secret shares of undesired messages as a side information by means of storing the secret shares at multiple databases. In particular, it is shown that the rates of the proposed PIR schemes are within a constant multiplicative factor from the derived upper-bound on the capacity of PIR problem.

A new relay-aided non-orthogonal multiple access (NOMA) technique is proposed for multi-cell uplink cellular networks in which each cell supports K single-antenna users by its respective base station (BS) equipped with N(4Z ≪K) antennas. Cooperative relaying transmission is used to accommodate more than one user per orthogonal resource block in the context of interference-limited cellular networks. With the proposed relayaided NOMA, an Alamouti structure of the desired symbol can further be generated at each BS free of interference, which gives rise to diversity gain of two. The proposed scheme does not require any channel state information (CSI) at users. Further, only limited CS! is required at relays and BSs, which can greatly reduce the control overhead.

We study the symmetric degrees-of-freedom (DoF) of partially connected interference networks under linear coding strategies without channel state information at the transmitters beyond topology. We assume that the receivers are equipped with reconfigurable antennas that can switch among their preset modes. In such a network setting, we characterize the class of network topologies in which half linear symmetric DoF is achievable. Moreover, we derive two general upper bounds on the linear symmetric DoF for arbitrary network topologies. We also demonstrate the tightness of our bounds for the class of network topologies in which all the transmitters in the network have at most two co-interferers.

In this paper, we characterize the degrees of freedom (DoF) for K-user M × 1 multiple-input single-output interference channels with reconfigurable antennas, which have N-preset modes at the receivers, assuming linear coding strategies in the absence of channel state information at the transmitters, i.e., blind interference alignment. Our linear DoF converse builds on the lemma that if a set of transmit symbols is aligned at their common unintended receivers, those symbols must have independent signal subspace at their corresponding receivers. This lemma arises from the inherent feature that channel state's changing patterns of the links towards the same receiver are always identical, assuming that the coherence time of the channel is long enough. We derive an upper bound for the linear sum DoF, and propose an achievable scheme that exactly achieves the linear sum DoF upper bound when both of the n*/M = R1and MK/n* = R2are integers, where n* denotes the optimal number of preset modes out of N preset modes. For the other cases, where either R1or R2is not an integer, we only give some guidelines how the interfering signals are aligned at the receivers to achieve the upper bound. As an extension, we also show the linear sum DoF upper bound for downlink/uplink cellular networks.

In this paper, we characterize the linear degrees of freedom (DoF) of a cellular network in which the base station (BS) operates in a full-duplex (FD) mode and the users operate in a half-duplex mode. We assume that the BS and the users are equipped with reconfigurable antennas which can be switched between their preset modes. We consider two practical scenarios for different assumptions on channel state information at the transmit sides (CSIT), referred to as no CSIT and partial CSIT models. To derive the inner-bounds for two scenarios, we propose a new achievable scheme which enables interference alignment between uplink and downlink interference signals at each user via preset mode switching of reconfigurable antennas. The key concept of our scheme is to align the interference signals of uplink transmission at the downlink users, through the identical preset mode pattern over the multiple of downlink transmission periods and silence periods of the BS. We also develop an outer-bound on the linear sum DoF of the cellular network for the no CSIT model, which matches up with the inner-bound. Moreover, we also provide a natural variant of the proposed scheme when considering residual self-interference at the FD BS, which can alleviate the shortcoming of the existing self-interference cancellation techniques.

In this paper, we propose a novel relay-aided successive aligned interference cancellation (RaSAIC) method for a 2 × K X channel with L full-duplex relays. The key idea behind the RaSAIC is to apply a cooperative relays' precoding technique that aligns interference signals at unintended receivers while creating an Alamouti structure for the desired signals. With the proposed cooperative relay transmission technique, it is shown that both the optimal degrees of freedom gain of 2K/K+1 and the diversity gain of two are achievable for constant channels when the numbers of relays' antennas are enough to satisfy the derived feasibility conditions without requiring channel state information (CSI) at transmitters but with global and local CSI at relays and receivers, respectively.

We study the symmetric degrees-of-freedom (DoF) of partially connected interference networks under linear coding strategies without channel state information at the transmitters beyond topology. We assume that the receivers are equipped with reconfigurable antennas that can switch among their preset modes. In such a network setting, we characterize the class of network topologies in which half linear symmetric DoF is achievable. Moreover, we derive a general upper bound on the linear symmetric DoF for arbitrary network topologies. We also show that this upper bound is tight if the transmitters have at most two co-interferers.

We develop a blind interference alignment (BIA) through staggered antenna switching scheme for more realistic channel assumption. Contrary to the general assumption that the coherence time of the channel is long enough to perform BIA, when the coherence time is not long enough, channel coefficients stay constant for limited block length. Therefore, we propose a dynamic supersymbol design algorithm which can construct a supersymbol with limited block length which is determined by the coherence time of the channel. We demonstrate that the supersymbol length can be reduced significantly by aligning interferences in a hierarchical manner referred to as hierarchical BIA. Furthermore, we also show that the proposed dynamic supersymbol design algorithm achieves higher degrees of freedom than the conventional method with a given coherence time.

We propose a blind interference alignment (BIA) through staggered antenna switching scheme with no ideal channel assumption. Contrary to the ideal assumption that channels remain constant during BIA symbol extension period, when the coherence time of the channel is relatively short, channel coefficients may change during a given symbol extension period. To perform BIA perfectly with realistic channel assumption, we propose a grouping based supersymbol structure for K-user interference channels which can adjust a supersymbol length to given coherence time. It is proved that the supersymbol length could be reduced significantly by an appropriate grouping. Furthermore, it is also shown that the grouping based supersymbol achieves higher degrees of freedom than the conventional method with given coherence time.