# Rebirth of Distributed AI--A Review of eHealth Research
## 1 Introduction  
Smart cities aim at providing robust solutions to crucial societal challenges related to transportation, health, environment, education, and security [1]. Smart cities are expected to deploy massive Internet-of-Things (IoT) related devices and applications. It is estimated that 75 billion devices will be connected by 2025, a jump from 31 billion in 2020 [2]. Moreover, the amount of data generated from devices or humans is growing every day. According to estimates, 4 TB of data will be generated by the self-driving car per day [3] and the amount of data created over the next three years will be more than the data created over the past 30 years [4]. With this explosion of big data and the proliferation of cheaper sensors and mobile devices, along with the advancement in both wireless-communication and deep learning, we envision a wide spectrum of different applications that would revolutionize many industries, businesses, services, and our day-to-day lives. These applications will be characterized by being data-driven and distributed. Learning from the data, in the case of the smart city operations, enables the stakeholders to reinforce and optimize the performance of various smart city operations. However, a larger portion of such data is usually generated by users who are sensitive to privacy infringement [5]. Even if the data privacy concern is addressed, yet another challenge remains, which is sending the massive volumes of data to a central server that may heavily congest the communication bit-pipes [6]. Furthermore, another pain point of AI's applications in smart cities is the training time of the ML model. Even with the availability of massive computing power and the evolution of deep neural networks, it is impossible to perform the most complex analyses without going through the time consuming phase of pre-processing and feature selection. To tackle these and similar challenges, the concept of distributed learning turns out to be a promising solution. It will not only assist in mitigating the congestion on thecommunication bit-pipes but also enable the implementation of collaborative models without compromising the privacy of the data. Achieving the required features for the envisioned smart city applications require a paradigm shift in the current technologies. We are already witnessing a shift from centralized data-computing, whether on-premises or in the cloud, to a distributed data-computing, namely, edge computing. As per [7], edge computing will not substitute the cloud, but its adoption is definitely growing. The need of edge computing is inevitable with the pervasiveness of the IoT and the increase in data generated, especially the ephemeral data that should not be transferred to the cloud. Moreover, edge computing is necessary due to the need for real-time and on-spot processing such as in life-critical real-time health monitoring applications. Federated Learning (FL) addresses the aforementioned and similar problems [8]. It allows stakeholders to train the model with their local datasets without violating their privacy. The stakeholders then share the hyper parameters or gradients of their locally trained model with the orchestrating central server [9]. The server keeps updating the central model through captured hyper parameter values from the clients. This is to say that each client feeds in to the global model at the central server and then downloads the updated global model. The central server keeps looping this process until the learning is matured [10]. The recent concept of Mobile Edge Computing (MEC) has further added to the impact of federated learning by enabling the placement of the central model nearer to the device layer, which is an important achievement when it comes to realizing more complex use-case scenarios requiring active learning and real-time decisions for critical maneuvers.