Enhancing communication efficiency in federated machine learning: Current trends and future directions

Artificial Intelligence is an incessantly evolving and progressing technology. Machines can exhibit humanoid intelligence, every now and then even better than humans. Its models are being extensively used in today's sphere. It is due to the supremacy of data and artificial intelligence. However, in Machine Learning, the data is amassed through numerous devices, including smart gadgets like smart watches, laptops, mobile phones, etc., and is transported to a centralized server. These devices are also called edge devices. When the personal data of users is transferred to the servers where it is used to train models it creates issues related to data privacy. Federated Learning (FL) is contemporary to technologies like Blockchain, on-device AI, IOT, and Edge computing where private/personal data of the user is not sent on the server, rather training of models is carried out locally, on each device. While the nodes are learning continuous communication between them takes place in Federated Learning. Therefore, high bandwidth connections that can exchange parameters of the machine learning model along with adequate computing power and memory of the local devices are desired. Therefore, one of the major challenges involved in FL includes communication efficiency. This paper presents a survey about FL and discusses the recent developments related to overcoming constraints in communication efficiency in Federated Machine Learning.