Communication Efficiency in Federated Learning: Achievements and Challenges

Abstract

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.

References in corpus (9)

• HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning
• Threats to Federated Learning: A Survey
• BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning
• Peer-to-peer Federated Learning on Graphs
• One-Shot Federated Learning
• AIDE: Fast and Communication Efficient Distributed Optimization
• Federated Learning Of Out-Of-Vocabulary Words
• Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies
• Hyper-Sphere Quantization: Communication-Efficient SGD for Federated Learning