Federated Learning (FL) is known to perform Machine Learning tasks in a distributed manner. Over the years, this has become an emerging technology especially with various data protection and privacy policies being imposed FL allows performing machine learning tasks whilst adhering to these challenges. As with the emerging of any new technology, there are going to be challenges and benefits. A challenge that exists in FL is the communication costs, as FL takes place in a distributed environment where devices connected over the network have to constantly share their updates this can create a communication bottleneck. In this paper, we present a survey of the research that is performed to overcome the communication constraints in an FL setting.

翻译：众所周知,联邦学习联合会(FL)以分散方式执行机器学习任务,多年来,这已成为一种新兴技术,特别是随着各种数据保护和隐私政策被强制推行,FL允许在坚持这些挑战的同时执行机器学习任务,与任何新技术的出现一样,将面临挑战和好处,FL存在的一个挑战是通信成本,因为FL是在分布式环境中发生的,因为连接网络的装置在分布式环境中不断分享其最新消息,这可能造成通信瓶颈。我们在本文件中对为克服FL环境中的通信限制而进行的研究进行了调查。