# A Systematic Literature Review on Distributed Machine Learning in Edge Computing
## 4 Answering the RQs
### RQ2--Techniques and Strategies
#### 4.2.9 Other Techniques
The result is fed to the machine-learning-based NILM classifiers. The training process can be completed on a computer to handle a large amount of information, but it should be guaranteed that the trained classifiers can be applied in the microcontrollers of smart plugs. Two distributed machine learning methods based on Deep Reinforcement Learning (DRL) were implemented by [PERSON] et al. [38]: edge DRL and coordinated DRL. The system is distributed over the network. In the first approach, the entire learning system is implemented at the edge layer. In the coordinated strategy, the cloud processes a DNN and its outputs become the input of a Q-Learning method implemented at the border. So, there is a hybrid method with reinforcement learning that is operating on edge devices. These methods were compared with the system completely implemented in the cloud. Coordinated DRL showed less resource consumption and less delay. [PERSON] et al. [120] propose a Lightweight Residual Network (LRN) architecture and a framework for image de-raining on resource-constrained edge cameras. The LRN can improve the visual quality of images under heavy rain at the expense of marginally compromising the Peak Signal to Noise Ratio (PSNR). To decrease the network complexity, they employ an Inverted Residual Block (IRB) [121] as the basic building block, with significantly low computational cost as it replaces standard convolution with a sequence of pointwise convolutions and depthwise convolutions. A pointwise convolutional layer expands the number of input feature maps, and then a depthwise convolutional layer extracts features, which are linearly combined by the last pointwise convolutional layer. In order to keep space complexity low, each layer uses stride 2 to down-sample the feature maps into a smaller size, then upsample them back using deconvolutional layers. [PERSON] et al. [122] implement a novel NN architecture to integrate industrial Domain Knowledge (DK) with machine learning in the context of Prognostic Health Management (PHM) solutions for industrial applications. A distributed computational architecture for motion control with edge intelligence and cloud processing is adopted. [PERSON] and [PERSON] [123] study how to improve the overall reliability of the time-critical object detection and classification in MEC with the imperfect transmission, where multiple user equipment and edge servers are present, and a certain level of image distortion is tolerated. The authors then formulate an optimization problem to maximize the overall service reliability under latency constraints. The Semidefinite Relaxation (SDR)-based algorithm is designed to find a solution for the association between the user equipment and edge servers and communication and computing resource allocation. The performance of the proposed algorithm is evaluated using the practical object detection methods (SSD [124] and YOLOv2 [125]) in multiple scenarios, showing that the SDR-based algorithm performs similarly to the exhaustive method, at a much lower complexity. Distributed deep learning applications in an osmotic computing environment are presented by [PERSON] et al. [70]. The authors focus on biomedical applications, presentinga Holistic Distributed Deep Learning (HDDL) approach to provide the integration of different data and the orchestration of mobile edge, edge and cloud computing in distributed deep learning applications. Some of the main challenges depicted for the development of HDDL reside in how to provide semantic interoperability, privacy, data quality, and volume. The authors propose a high-level HDDL architecture to integrate different systems, especially the biomedical applications that are highlighted in the paper.