Equitable-FL: Federated Learning with Sparsity for Resource-Constrained Environment

In Federated Learning, model training is performed across multiple computing devices, where only parameters are shared with a common central server without exchanging their data instances. This strategy assumes abundance of resources on individual clients and utilizes these resources to build a richer model as user's models. However, when the assumption of the abundance of resources is violated, learning may not be possible as some nodes may not be able to participate in the process. In this paper, we propose a sparse form of federated learning that performs well in a Resource Constrained Environment. Our goal is to make learning possible, regardless of a node's space, computing, or bandwidth scarcity. The method is based on the observation that model size viz a viz available resources defines resource scarcity, which entails that reduction of the number of parameters without affecting accuracy is key to model training in a resource-constrained environment. In this work, the Lottery Ticket Hypothesis approach is utilized to progressively sparsify models to encourage nodes with resource scarcity to participate in collaborative training. We validate Equitable-FL on the $MNIST$, $F-MNIST$, and $CIFAR-10$ benchmark datasets, as well as the $Brain-MRI$ data and the $PlantVillage$ datasets. Further, we examine the effect of sparsity on performance, model size compaction, and speed-up for training. Results obtained from experiments performed for training convolutional neural networks validate the efficacy of Equitable-FL in heterogeneous resource-constrained learning environment.Comment: 12 pages, 7 figure

A Practical View on Training Neural Networks in the Edge

In recent years, the topic of embedded machine learning has become very popular in AI research. With the help of various compression techniques such as pruning, quantization and others compression techniques, it became possible to run neural networks on embedded devices. These techniques have opened up a whole new application area for machine learning. They range from smart products such as voice assistants to smart sensors that are needed in robotics. Despite the achievements in embedded machine learning, efficient algorithms for training neural networks in constrained domains are still lacking. Training on embedded devices will open up further fields of applications. Efficient training algorithms would enable federated learning on embedded devices, in which the data remains where it was collected, or retraining of neural networks in different domains. In this paper, we summarize techniques that make training on embedded devices possible. We first describe the need and requirements for such algorithms. Then we examine existing techniques that address training in resource-constrained environments as well as techniques that are also suitable for training on embedded devices, such as incremental learning. At the end, we also discuss which problems and open questions still need to be solved in these areas