Version 1
: Received: 27 September 2022 / Approved: 28 September 2022 / Online: 28 September 2022 (09:06:58 CEST)
How to cite:
Richter, L.; Dontsov, I.; Jacob, T. Ensemble Weighting Strategy For Federated Learning To Handle Heterogeneous Data Distributions. Preprints2022, 2022090435. https://doi.org/10.20944/preprints202209.0435.v1
Richter, L.; Dontsov, I.; Jacob, T. Ensemble Weighting Strategy For Federated Learning To Handle Heterogeneous Data Distributions. Preprints 2022, 2022090435. https://doi.org/10.20944/preprints202209.0435.v1
Richter, L.; Dontsov, I.; Jacob, T. Ensemble Weighting Strategy For Federated Learning To Handle Heterogeneous Data Distributions. Preprints2022, 2022090435. https://doi.org/10.20944/preprints202209.0435.v1
APA Style
Richter, L., Dontsov, I., & Jacob, T. (2022). Ensemble Weighting Strategy For Federated Learning To Handle Heterogeneous Data Distributions. Preprints. https://doi.org/10.20944/preprints202209.0435.v1
Chicago/Turabian Style
Richter, L., Ilja Dontsov and Tania Jacob. 2022 "Ensemble Weighting Strategy For Federated Learning To Handle Heterogeneous Data Distributions" Preprints. https://doi.org/10.20944/preprints202209.0435.v1
Abstract
Increasingly measured data in the context of smart cities can be used to develop new and innovative business models to increase efficiency and the value of life. A time-series classification algorithm can support to automatize many different processes such as forecasting services. In order to ensure data security and privacy, Federated Learning trains a global model collaboratively on multiple clients. Having different data-distributions and data-quantities across participating clients, neural networks suffer from slow convergence and overfitting. Based on different data-distributions, data-quantities and number of clients, we develop and evaluate different data-clustering strategies to update global model weights in comparison to the state of the art. We use public time-series data, generate various synthetic datasets and train a Relational-Regularized Autoencoder for classification purposes. Our results show an improvement of model performance concerning generalization.
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.