Multi-party computation (MPC) is promising for designing privacy-preserving machine learning algorithms at edge networks. An emerging approach is coded-MPC (CMPC), which advocates the use of coded computation to improve the performance of MPC in terms of the required number of workers involved in computations. The current approach for designing CMPC algorithms is to merely combine efficient coded computation constructions with MPC. We show that this approach fails short of being efficient; e.g., entangled polynomial codes are not necessarily better than PolyDot codes in MPC setting, while they are always better for coded computation. Motivated by this observation, we propose a new construction; Adaptive Gap Entangled (AGE) polynomial codes for MPC. We show through analysis and simulations that MPC with AGE codes always perform better than existing CMPC algorithms in terms of the required number of workers as well as computation, storage, and communication overhead.

Cooperative computation is a promising approach for localized data processing at the edge, e.g., for Internet of Things(IoT). Cooperative computation advocates that computationally intensive tasks in a device could be divided into sub-tasks, and offloaded to other devices or servers in close proximity. However, exploiting the potential of cooperative computation is challenging mainly due to the heterogeneous and time-varying nature of edge devices. Coded computation, which advocates mixing data in sub-tasks by employing erasure codes and offloading these sub-tasks to other devices for computation, is recently gaining interest, thanks to its higher reliability, smaller delay, and lower communication costs. In this paper, we develop a coded cooperative computation framework, which we name Coded Cooperative Computation Protocol (C3P), by taking into account the heterogeneous and time-varying resources of edge devices. C3P dynamically offloads coded sub-tasks to helpers and is adaptive to time-varying resources. We show that (i) task completion delay of C3P is very close to optimal coded cooperative computation solutions, (ii) the efficiency of C3P in terms of resource utilization is higher than 99%, and (iii) C3P improves task completion delay significantly as compared to baselines via both simulations and in a testbed consisting of real Android-based smartphones.

Multi-party computation (MPC) is promising for designing privacy-preserving machine learning algorithms at edge networks. An emerging approach is coded-MPC (CMPC), which advocates the use of coded computation to improve the performance of MPC in terms of the required number of workers involved in computations. The current approach for designing CMPC algorithms is to merely combine efficient coded computation constructions with MPC. Instead, we propose a new construction; Adaptive Gap Entangled polynomial (AGE) codes, where the degrees of polynomials used in computations are optimized for MPC. We show that MPC with AGE codes (AGE-CMPC) perform better than existing CMPC algorithms in terms of the required number of workers as well as storage, communication and computation load.

Robust and computationally efficient anomaly detection in videos is a problem in video surveillance systems. We propose a technique to increase robustness and reduce computational complexity in a Convolutional Neural Network (CNN) based anomaly detector that utilizes the optical flow information of video data. We reduce the complexity of the network by denoising the intermediate layer outputs of the CNN and by using powers-of-two weights, which replaces the computationally expensive multiplication operations with bit-shift operations. Denoising operation during inference forces small valued intermediate layer outputs to zero. The number of zeros in the network significantly increases as a result of denoising, we can implement the CNN about 10% faster than a comparable network while detecting all the anomalies in the testing set. It turns out that denoising operation also provides robustness because the contribution of small intermediate values to the final result is negligible. During training we also generate motion vector images by a Generative Adversarial Network (GAN) to improve the robustness of the overall system. We experimentally observe that the resulting system is robust to background motion.

Edge computing is emerging as a new paradigm to allow processing data at the edge of the network, where data is typically generated and collected by exploiting multiple devices at the edge collectively. However, offloading tasks to other devices leaves the edge computing applications at the complete mercy of an attacker. One of the attacks, which is the focus of this work, is Byzantine attacks, where one or more devices can corrupt the offloaded tasks. Furthermore, exploiting the potential of edge computing is challenging mainly due to the heterogeneous and time-varying nature of the devices at the edge. In this paper, we develop a secure coded cooperative computation mechanism (SC3) that provides both security and computation efficiency guarantees by gracefully combining homomorphic hash functions and coded cooperative computation. Homomorphic hash functions are used against Byzantine attacks and coded cooperative computation is used to improve computation efficiency when edge resources are heterogeneous and time-varying. Simulation results show that SC3 improves task completion delay significantly.

Cooperative computation is a promising approach for localized data processing at the edge, e.g., for Internet of Things (IoT). Cooperative computation advocates that computationally intensive tasks in a device could be divided into sub-tasks, and offloaded to other devices or servers in close proximity. However, exploiting the potential of cooperative computation is challenging mainly due to the heterogeneous and time-varying nature of edge devices. Coded computation, which advocates mixing data in sub-tasks by employing erasure codes and offloading these sub-tasks to other devices for computation, is recently gaining interest, thanks to its higher reliability, smaller delay, and lower communication costs. In this paper, we develop a coded cooperative computation framework, which we name Coded Cooperative Computation Protocol (C3P), by taking into account the heterogeneous resources of edge devices. C3P dynamically offloads coded sub-tasks to helpers and is adaptive to time-varying resources. We show that (i) task completion delay of C3P is very close to optimal coded cooperative computation solutions, (ii) the efficiency of C3P in terms of resource utilization is higher than 99%, (iii) C3P improves task completion delay significantly as compared to baselines via both simulations and in a test-bed consisting of real Android-based smartphones.

Consider a scenario in which a source broadcasts a common content to a group of cooperating mobile devices that are within proximity of each other. Devices in this group may receive only partial content from the source due to packet losses over wireless broadcast links and these packet losses may differ for different devices. The remaining content missing at each device can then be recovered, thanks to cooperation among the devices by exploiting device-to-device (D2D) connections. In this context, the minimum amount of time that guarantees a complete acquisition of the common content at every device is referred to as the “completion time”. It has been shown that instantly decodable network coding (IDNC) reduces the completion time as compared with no network coding in this scenario. However, for applications such as video streaming, not all packets have the same importance and not all devices are interested in the same quality of content. This problem becomes more interesting and challenging when additional, but realistic constraints, such as strict deadline, bandwidth, or limited energy are added in the problem formulation. We assert that direct application of IDNC in such a scenario yields poor performance in terms of content quality and completion time. In this paper, we propose a novel Content- and Loss-Aware IDNC scheme that improves content quality and network coding opportunities jointly by taking into account the contribution of each packet to the desired quality of service (QoS) as well as the channel losses over D2D links. Our proposed Content- and Loss-Aware IDNC (i) maximizes the quality under the completion time constraint, and (ii) minimizes the completion time under the quality constraint. We demonstrate the benefits of Content- and Loss-Aware IDNC through simulations.

In this paper, a scheme based on 1-D nested lattice quantization followed by multi-level distributed arithmetic coding (MLDAC) as the Slepian-Wolf (SW) code is proposed for the lossy source coding of continuous sources. This system can be employed in distributed video and image coding applications. The output of the quantizer is first converted to binary, and then the SW coding is applied on each bit plane. An efficient algorithm for joint decoding of bit planes at the decoder is proposed, which exploits the dependency of the bit planes with each other and the side-information available at the decoder. Also, a methodology for rate allocation among the bit planes is presented. The quantization parameters are designed to improve the end-to-end system performance based on its overall rate-distortion function taking into account both the distortions due to quantization and imperfect practical Slepian-Wolf coding. The simulation results demonstrate the effectiveness of the proposed MLDAC with the lattice quantization.