Data Driven Bottom up Asynchronous Federated Learning
Federated learning (FL) is a distributed machine learning approach that can train a model on decentralized sets of data without the data ever leaving their host devices. The predominant FL model is the Google FL protocol (GFL) which is a topdown, synchronous and time division resource allocation system. It faces several issues, such as increased bias in selecting data and inability to scale beyond a small number of devices. In this paper, we design and investigate an alternative FL architecture to address the challenges of GFL. Our system is a bottom up, data driven, asynchronous resource allocation system, that maximizes key resources consisting of samples, computation, and bandwidth. The performance of our system is evaluated with the MNIST dataset on a networked prototype system. The experimental results show that our system improves fairness and scalability without compromising convergence efficiency.