Publications
374 results found
Yang Z, Xia J-Y, Luo J, et al., 2022, A learning-aided flexible gradient descent approach to MISO beamforming, IEEE Wireless Communications Letters, Vol: 11, Pages: 1895-1899, ISSN: 2162-2337
This letter proposes a learning aided gradient descent (LAGD) algorithm to solve the weighted sum rate (WSR) maximization problem for multiple-input single-output (MISO) beamforming. The proposed LAGD algorithm directly optimizes the transmit precoder through implicit gradient descent based iterations, at each of which the optimization strategy is determined by a neural network, and thus, is dynamic and adaptive. At each instance of the problem, this network is initialized randomly, and updated throughout the iterative solution process. Therefore, the LAGD algorithm can be implemented at any signal-to-noise ratio (SNR) and for arbitrary antenna/user numbers, does not require labelled data or training prior to deployment. Numerical results show that the LAGD algorithm can outperform of the well-known WMMSE algorithm as well as other learning-based solutions with a modest computational complexity. Our code is available at https://github.com/XiaGroup/LAGD .
Li GY, Saad W, Ozgur A, et al., 2022, Series Editorial The Sixth Issue of the Series on Machine Learning in Communications and Networks, IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, Vol: 40, Pages: 2507-2509, ISSN: 0733-8716
Ozfatura E, Shao Y, Perotti AG, et al., 2022, All You Need Is Feedback: Communication With Block Attention Feedback Codes, IEEE Journal on Selected Areas in Information Theory, Vol: 3, Pages: 587-602
Tung T-Y, Gunduz D, 2022, DeepWiVe: deep-learning-aided wireless video transmission, IEEE Journal on Selected Areas in Communications, Vol: 40, Pages: 2570-2583, ISSN: 0733-8716
We present DeepWiVe , the first-ever end-to-end joint source-channel coding (JSCC) video transmission scheme that leverages the power of deep neural networks (DNNs) to directly map video signals to channel symbols, combining video compression, channel coding, and modulation steps into a single neural transform. Our DNN decoder predicts residuals without distortion feedback, which improves the video quality by accounting for occlusion/disocclusion and camera movements. We simultaneously train different bandwidth allocation networks for the frames to allow variable bandwidth transmission. Then, we train a bandwidth allocation network using reinforcement learning (RL) that optimizes the allocation of limited available channel bandwidth among video frames to maximize the overall visual quality. Our results show that DeepWiVe can overcome the cliff-effect , which is prevalent in conventional separation-based digital communication schemes, and achieve graceful degradation with the mismatch between the estimated and actual channel qualities. DeepWiVe outperforms H.264 video compression followed by low-density parity check (LDPC) codes in all channel conditions by up to 0.0485 in terms of the multi-scale structural similarity index measure (MS-SSIM), and H.265+ LDPC by up to 0.0069 on average. We also illustrate the importance of optimizing bandwidth allocation in JSCC video transmission by showing that our optimal bandwidth allocation policy is superior to uniform allocation as well as a heuristic policy benchmark.
Yilmaz SF, Hasircioglu B, Gunduz D, 2022, Over-the-air ensemble inference with model privacy, 2022 IEEE International Symposium on Information Theory (ISIT), Publisher: IEEE, Pages: 1265-1270
We consider distributed inference at the wireless edge, where multiple clients with an ensemble of models, each trained independently on a local dataset, are queried in parallel to make an accurate decision on a new sample. In addition to maximizing inference accuracy, we also want to maximize the privacy of local models. We exploit the superposition property of the air to implement bandwidth-efficient ensemble inference methods. We introduce different over-the-air ensemble methods and show that these schemes perform significantly better than their orthogonal counterparts, while using less resources and providing privacy guarantees. We also provide experimental results verifying the benefits of the proposed over-the-air inference approach, whose source code is shared publicly on Github.
Egger M, Bitar R, Wachter-Zeh A, et al., 2022, Efficient distributed machine learning via combinatorial multi-armed bandits, 2022 IEEE International Symposium on Information Theory (ISIT), Publisher: IEEE, Pages: 1653-1658
We consider the distributed stochastic gradient descent problem, where a main node distributes gradient calculations among n workers from which at most b ≤ n can be utilized in parallel. By assigning tasks to all the workers and waiting only for the k fastest ones, the main node can trade-off the error of the algorithm with its runtime by gradually increasing k as the algorithm evolves. However, this strategy, referred to as adaptive k-sync, can incur additional costs since it ignores the computational efforts of slow workers. We propose a cost-efficient scheme that assigns tasks only to k workers and gradually increases k. As the response times of the available workers are unknown to the main node a priori, we utilize a combinatorial multi-armed bandit model to learn which workers are the fastest while assigning gradient calculations, and to minimize the effect of slow workers. Assuming that the mean response times of the workers are independent and exponentially distributed with different means, we give empirical and theoretical guarantees on the regret of our strategy, i.e., the extra time spent to learn the mean response times of the workers. Compared to adaptive k-sync, our scheme achieves significantly lower errors with the same computational efforts while being inferior in terms of speed.
Pase F, Gunduz D, Zorzi M, 2022, Remote contextual bandits, 2022 IEEE International Symposium on Information Theory (ISIT), Publisher: IEEE, Pages: 1665-1670
We consider a remote contextual multi-armed bandit (CMAB) problem, in which the decision-maker observes the context and the reward, but must communicate the actions to be taken by the agents over a rate-limited communication channel. This can model, for example, a personalized ad placement application, where the content owner observes the individual visitors to its website, and hence has the context information, but must convey the ads that must be shown to each visitor to a separate entity that manages the marketing content. In this remote CMAB (R-CMAB) problem, the constraint on the communication rate between the decision-maker and the agents imposes a trade-off between the number of bits sent per agent and the acquired average reward. We are particularly interested in characterizing the rate required to achieve sub-linear regret. Consequently, this can be considered as a policy compression problem, where the distortion metric is induced by the learning objectives. We first study the fundamental information theoretic limits of this problem by letting the number of agents go to infinity, and study the regret achieved when Thompson sampling strategy is adopted. In particular, we identify two distinct rate regions resulting in linear and sub-linear regret behavior, respectively. Then, we provide upper bounds for the achievable regret when the decision-maker can reliably transmit the policy without distortion.
ul Haque S, Chandak S, Chiariotti F, et al., 2022, Learning to speak on behalf of a group: medium access control for sending a shared message, IEEE Communications Letters, Vol: 26, Pages: 1843-1847, ISSN: 1089-7798
The rapid development of Internet of Things (IoT) technologies has not only enabled new applications, but also presented new challenges for reliable communication with limited resources. In this work, we define a novel problem that can arise in these scenarios, in which a set of sensors need to communicate a joint observation. This observation is shared by a random subset of the nodes, which need to propagate it to the rest of the network, but coordination is complex: as signaling constraints require the use of random access schemes over shared channels, sensors need to implicitly coordinate, so that at least one transmission gets through without collisions. Unlike the majority of existing medium access schemes, the goal is to make sure that the shared message gets through, regardless of the sender. We analyze this coordination problem theoretically and provide low-complexity solutions. While a clustering-based approach is near-optimal if the sensors have prior knowledge, we provide a distributed multi-armed bandit (MAB) solution for the more general case and validate it by simulation.
Li GY, Saad W, Ozgur A, et al., 2022, Series Editorial The Fifth Issue of the Series on Machine Learning in Communications and Networks, IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, Vol: 40, Pages: 2251-2253, ISSN: 0733-8716
Shiri I, Sadr AV, Amini M, et al., 2022, Decentralized Distributed Multi-institutional PET Image Segmentation Using a Federated Deep Learning Framework, CLINICAL NUCLEAR MEDICINE, Vol: 47, Pages: 606-617, ISSN: 0363-9762
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- Citations: 11
Sun Y, Zhang F, Zhao J, et al., 2022, Coded computation across shared heterogeneous workers with communication delay, IEEE Transactions on Signal Processing, Vol: 70, Pages: 3371-3385, ISSN: 1053-587X
Distributed computing enables large-scale computation tasks to be processed by multiple workers in parallel. However, the randomness of communication and computation delays across the workers causes the straggler effect, which may degrade the delay performance. Coded computation helps to mitigate the straggler effect, but the amount of redundant load and task assignment to the workers should be carefully optimized. In this work, we consider a multi-master heterogeneous-worker distributed computing scenario, where multiple matrix multiplication tasks are encoded and allocated to the workers with different computing capabilities. The goal is to minimize the communication plus computation delay of all the tasks. We propose joint worker assignment, resource allocation and load allocation algorithms under both dedicated and fractional worker assignment policies, where each worker can process the encoded tasks from either a single master or multiple masters, respectively. Then, the non-convex delay minimization problem is solved by employing the Markov’s inequality-based approximation, Karush-Kuhn-Tucker conditions, and successive convex approximation methods. Through extensive simulations, we show that the proposed algorithms can reduce the task completion delay compared to the benchmarks.
Shao Y, Gunduz D, Liew SC, 2022, Federated edge learning with misaligned over-the-air computation, IEEE Transactions on Wireless Communications, Vol: 21, Pages: 1-1, ISSN: 1536-1276
Over-the-air computation (OAC) is a promising technique to realize fast model aggregation in the uplink of federated edge learning (FEEL). OAC, however, hinges on accurate channel-gain precoding and strict synchronization among edge devices, which are challenging in practice. As such, how to design the maximum likelihood (ML) estimator in the presence of residual channel-gain mismatch and asynchronies is an open problem. To fill this gap, this paper formulates the problem of misaligned OAC for FEEL and puts forth a whitened matched filtering and sampling scheme to obtain oversampled, but independent samples from the misaligned and overlapped signals. Given the whitened samples, a sum-product ML (SP-ML) estimator and an aligned-sample estimator are devised to estimate the arithmetic sum of the transmitted symbols. In particular, the computational complexity of our SP-ML estimator is linear in the packet length, and hence is significantly lower than the conventional ML estimator. Extensive simulations on the test accuracy versus the average received energy per symbol to noise power spectral density ratio (EsN0) yield two main results: 1) In the low EsN0 regime, the aligned-sample estimator can achieve superior test accuracy provided that the phase misalignment is not severe. In contrast, the ML estimator does not work well due to the error propagation and noise enhancement in the estimation process. 2) In the high EsN0 regime, the ML estimator attains the optimal learning performance regardless of the severity of phase misalignment. On the other hand, the aligned-sample estimator suffers from a test-accuracy loss caused by phase misalignment.
Teng F, Chhachhi SAURAB, Ge PUDONG, et al., 2022, Balancing privacy and access to smart meter data: an Energy Futures Lab briefing paper
Digitalising the energy system is expected to be a vital component of achieving the UK’s climate change targets. Smart meter data, in particular, is seen a key enabler of the transition to more dynamic, cost-effective, cost-reflective, and decarbonised electricity. However, access to this data faces a challenge due to consumer privacy concerns. This Briefing Paper investigates four key elements of smart meter data privacy: existing data protection regulations; the personal information embedded within smart meter data; consumer privacy concerns; and privacy-preserving techniques that could be incorporated alongside existing mechanisms to minimise or eliminate potential privacy infringements.
Ozfatura E, Gunduz D, 2022, Uncoded caching and cross-level coded delivery for non-uniform file popularity, IEEE Transactions on Information Theory, Vol: 68, Pages: 6842-6859, ISSN: 0018-9448
Proactive content caching at user devices and coded delivery is studied for a non-uniform file popularity distribution. A novel centralized uncoded caching and coded delivery scheme, called cross-level coded delivery (CLCD) , is proposed, which can be applied to large file libraries under non-uniform demands. In the CLCD scheme, the same sub-packetization is used for all the files in the library in order to prevent additional zero-padding in the delivery phase, and unlike the existing schemes in the literature, users requesting files from different popularity groups can still be served by the same multicast message in order to reduce the delivery rate. Simulation results indicate more than 10% reduction in the average delivery rate for typical Zipf distribution parameter values.
Mital N, Ling C, Gunduz D, 2022, Secure distributed matrix computation with discrete fourier transform, IEEE Transactions on Information Theory, Vol: 68, Pages: 1-1, ISSN: 0018-9448
We consider the problem of secure distributed matrix computation (SDMC), where a user queries a function of data matrices generated at distributed source nodes. We assume the availability of N honest but curious computation servers, which are connected to the sources, the user, and each other through orthogonal and reliable communication links. Our goal is to minimize the amount of data that must be transmitted from the sources to the servers, called the upload cost, while guaranteeing that no T colluding servers can learn any information about the source matrices, and the user cannot learn any information beyond the computation result. We first focus on secure distributed matrix multiplication (SDMM), considering two matrices, and propose a novel polynomial coding scheme using the properties of finite field discrete Fourier transform, which achieves an upload cost significantly lower than the existing results in the literature. We then generalize the proposed scheme to include straggler mitigation, and to the multiplication of multiple matrices while keeping the input matrices, the intermediate computation results, as well as the final result secure against any T colluding servers. We also consider a special case, called computation with own data, where the data matrices used for computation belong to the user. In this case, we drop the security requirement against the user, and show that the proposed scheme achieves the minimal upload cost. We then propose methods for performing other common matrix computations securely on distributed servers, including changing the parameters of secret sharing, matrix transpose, matrix exponentiation, solving a linear system, and matrix inversion, which are then used to show how arbitrary matrix polynomials can be computed securely on distributed servers using the proposed procedure
Zecchin M, Mashhadi MB, Jankowski M, et al., 2022, LIDAR and position-aided mmWave beam selection with non-local CNNs and curriculum training, IEEE Transactions on Vehicular Technology, Vol: 71, Pages: 2979-2990, ISSN: 0018-9545
Efficient millimeter wave (mmWave) beam selection in vehicle-to-infrastructure (V2I) communication is a crucial yet challenging task due to the narrow mmWave beamwidth and high user mobility. To reduce the search overhead of iterative beam discovery procedures, contextual information from light detection and ranging (LIDAR) sensors mounted on vehicles has been leveraged by data-driven methods to produce useful side information. In this paper, we propose a lightweight neural network (NN) architecture along with the corresponding LIDAR preprocessing, which significantly outperforms previous works. Our solution comprises multiple novelties that improve both the convergence speed and the final accuracy of the model. In particular, we define a novel loss function inspired by the knowledge distillation idea, introduce a curriculum training approach exploiting line-of-sight (LOS)/non-line-of-sight (NLOS) information, and we propose a non-local attention module to improve the performance for the more challenging NLOS cases. Simulation results on benchmark datasets show that, utilizing solely LIDAR data and the receiver position, our NN-based beam selection scheme can achieve 79.9% throughput of an exhaustive beam sweeping approach without any beam search overhead and 95% by searching among as few as 6 beams. In a typical mmWave V2I scenario, our proposed method considerably reduces the beam search time required to achieve a desired throughput, in comparison with the inverse fingerprinting and hierarchical beam selection schemes.
Hasircioglu B, Gomez-Vilardebo J, Gunduz D, 2022, Bivariate polynomial codes for secure distributed matrix multiplication, IEEE Journal on Selected Areas in Communications, Vol: 40, Pages: 955-967, ISSN: 0733-8716
We consider the problem of secure distributed matrix multiplication (SDMM). Coded computation has been shown to be an effective solution in distributed matrix multiplication, both providing privacy against workers and boosting the computation speed by efficiently mitigating stragglers. In this work, we present a non-direct secure extension of the recently introduced bivariate polynomial codes. Bivariate polynomial codes have been shown to be able to further speed up distributed matrix multiplication by exploiting the partial work done by the stragglers rather than completely ignoring them while reducing the upload communication cost and/or the workers’ storage’s capacity needs. We show that, especially for upload communication or storage constrained settings, the proposed approach reduces the average computation time of SDMM compared to its competitors in the literature.
Amiri MM, Gunduz D, Kulkarni SR, et al., 2022, Convergence of federated learning over a noisy downlink, IEEE Transactions on Wireless Communications, Vol: 21, Pages: 1422-1437, ISSN: 1536-1276
We study federated learning (FL), where power-limited wireless devices utilize their local datasets to collaboratively train a global model with the help of a remote parameter server (PS). The PS has access to the global model and shares it with the devices for local training using their datasets, and the devices return the result of their local updates to the PS to update the global model. The algorithm continues until the convergence of the global model. This framework requires downlink transmission from the PS to the devices and uplink transmission from the devices to the PS. The goal of this study is to investigate the impact of the bandwidth-limited shared wireless medium on the performance of FL with a focus on the downlink. To this end, the downlink and uplink channels are modeled as fading broadcast and multiple access channels, respectively, both with limited bandwidth. For downlink transmission, we first introduce a digital approach, where a quantization technique is employed at the PS followed by a capacity-achieving channel code to transmit the global model update over the wireless broadcast channel at a common rate such that all the devices can decode it. Next, we propose analog downlink transmission, where the global model is broadcast by the PS in an uncoded manner. We consider analog transmission over the uplink in both cases, since its superiority over digital transmission for uplink has been well studied in the literature. We further analyze the convergence behavior of the proposed analog transmission approach over the downlink assuming that the uplink transmission is error-free. Numerical experiments show that the analog downlink approach provides significant improvement over the digital one with a more notable improvement when the data distribution across the devices is not independent and identically distributed. The experimental results corroborate the convergence analysis, and show that a smaller number of local iterations should be used when
Chen M, Gunduz D, Huang K, et al., 2022, Guest Editorial Special Issue on Distributed Learning Over Wireless Edge Networks-Part II, IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, Vol: 40, Pages: 445-448, ISSN: 0733-8716
Elbir AM, Soner B, Coleri S, et al., 2022, Federated Learning in Vehicular Networks, Pages: 72-77
Machine learning (ML) has recently been adopted in vehicular networks for applications such as autonomous driving, road safety prediction and vehicular object detection, due to its model-free characteristic, allowing adaptive fast response. However, most of these ML applications employ centralized learning (CL), which brings significant overhead for data trans-mission between the parameter server and vehicular edge devices. Federated learning (FL) framework has been recently introduced as an efficient tool with the goal of reducing transmission overhead while achieving privacy through the transmission of model updates instead of the whole dataset. In this paper, we investigate the usage of FL over CL in vehicular network applications to develop intelligent transportation systems. We provide a comprehensive analysis on the feasibility of FL for the ML based vehicular applications, as well as investigating object detection by utilizing image-based datasets as a case study. Then, we identify the major challenges from both learning perspective, i.e., data labeling and model training, and from the communications point of view, i.e., data rate, reliability, transmission overhead, privacy and resource management. Finally, we highlight related future research directions for FL in vehicular networks.
Yemini M, Saha R, Ozfatura E, et al., 2022, Semi-Decentralized Federated Learning with Collaborative Relaying, Pages: 1471-1476, ISSN: 2157-8095
We present a semi-decentralized federated learning algorithm wherein clients collaborate by relaying their neighbors' local updates to a central parameter server (PS). At every communication round to the PS, each client computes a local consensus of the updates from its neighboring clients and eventually transmits a weighted average of its own update and those of its neighbors to the PS. We appropriately optimize these averaging weights to ensure that the global update at the PS is unbiased and to reduce the variance of the global update at the PS, consequently improving the rate of convergence. Numerical simulations substantiate our theoretical claims and demonstrate settings with intermittent connectivity between the clients and the PS, where our proposed algorithm shows an improved convergence rate and accuracy in comparison with the federated averaging algorithm.
Li GY, Saad W, Ozgur A, et al., 2022, Series Editorial The Fourth Issue of the Series on Machine Learning in Communications and Networks, IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, Vol: 40, Pages: 1-4, ISSN: 0733-8716
Tung T-Y, Gunduz D, 2022, Deep-Learning-Aided Wireless Video Transmission, 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC), Publisher: IEEE, ISSN: 2325-3789
Aygun O, Kazemi M, Gunduz D, et al., 2022, Hierarchical Over-the-Air Federated Edge Learning, IEEE International Conference on Communications (ICC), Publisher: IEEE, Pages: 3376-3381, ISSN: 1550-3607
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- Citations: 1
Hamidi SM, Mehrabi M, Khandani AK, et al., 2022, Over-the-Air Federated Learning Exploiting Channel Perturbation, 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC), Publisher: IEEE, ISSN: 2325-3789
Aygun O, Kazemi M, Gunduz D, et al., 2022, Over-the-Air Federated Learning with Energy Harvesting Devices, IEEE Global Communications Conference (GLOBECOM), Publisher: IEEE, Pages: 1942-1947, ISSN: 2334-0983
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- Citations: 1
Lan Q, Zeng Q, Popovski P, et al., 2022, Progressive Transmission of High-Dimensional Data Features for Inference at the Network Edge, 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC), Publisher: IEEE, ISSN: 2325-3789
Sun Y, Zhou S, Niu Z, et al., 2022, Time-Correlated Sparsification for Efficient Over-the-Air Model Aggregation in Wireless Federated Learning, IEEE International Conference on Communications (ICC), Publisher: IEEE, Pages: 3388-3393, ISSN: 1550-3607
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- Citations: 2
Tung T-Y, Kurka DB, Jankowski M, et al., 2022, DeepJSCC-Q: Channel Input Constrained Deep Joint Source-Channel Coding, IEEE International Conference on Communications (ICC), Publisher: IEEE, Pages: 3880-3885, ISSN: 1550-3607
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- Citations: 1
Ozfatura E, Ulukus S, Gunduz D, 2021, Coded distributed computing with partial recovery, IEEE Transactions on Information Theory, Vol: 68, Pages: 1945-1959, ISSN: 0018-9448
Coded computation techniques provide robustness against straggling workers in distributed computing. However, most of the existing schemes require exact provisioning of the straggling behavior and ignore the computations carried out by straggling workers. Moreover, these schemes are typically designed to recover the desired computation results accurately, while in many machine learning and iterative optimization algorithms, faster approximate solutions are known to result in an improvement in the overall convergence time. In this paper, we first introduce a novel coded matrix-vector multiplication scheme, called coded computation with partial recovery (CCPR), which benefits from the advantages of both coded and uncoded computation schemes, and reduces both the computation time and the decoding complexity by allowing a trade-off between the accuracy and the speed of computation. We then extend this approach to distributed implementation of more general computation tasks by proposing a coded communication scheme with partial recovery, where the results of subtasks computed by the workers are coded before being communicated. Numerical simulations on a large linear regression task confirm the benefits of the proposed scheme in terms of the trade-off between the computation accuracy and latency.
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