Neural network quantization in federated learning at the edge

Tonellotto N., Gotta A., Nardini F. M., Gadler D., Silvestri F.

Internet of things  Federated learning  Computer Science Applications  Artificial Intelligence  Theoretical Computer Science  Artificial neural networks  Quantization  Software  Information Systems and Management  Control and Systems Engineering 

The massive amount of data collected in the Internet of Things (IoT) asks for effective, intelligent analytics. A recent trend supporting the use of Artificial Intelligence (AI) solutions in IoT domains is to move the computation closer to the data, i.e., from cloud-based services to edge devices. Federated learning (FL) is the primary approach adopted in this scenario to train AI-based solutions. In this work, we investigate the introduction of quantization techniques in FL to improve the efficiency of data exchange between edge servers and a cloud node. We focus on learning recurrent neural network models fed by edge data producers using the most widely adopted neural networks for time-series prediction. Experiments on public datasets show that the proposed quantization techniques in FL reduces up to 19× the volume of data exchanged between each edge server and a cloud node, with a minimal impact of around 5% on the test loss of the final model.

Source: Information sciences 575 (2021): 417–436. doi:10.1016/j.ins.2021.06.039

Publisher: Elsevier [etc.], Boston [etc.], Paesi Bassi

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