Communication Efficiency in Federated Learning: Achievements and Challenges
It reviews existing solutions for communication efficiency in FL, which is crucial for scalability and privacy in distributed machine learning, but is incremental as a survey.
This paper surveys research addressing communication bottlenecks in Federated Learning, which arise from constant sharing of updates across distributed devices, but does not present new results or numbers.
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.