Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Ensemble Weighting Strategy For Federated Learning To Handle Heterogeneous Data Distributions

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. 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. Preprints 2022, 2022090435. 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.

Keywords

Federated Learning Strategies; Relational-Regularized Autoencoder; Time-Series Classification

Subject

Engineering, Electrical and Electronic Engineering

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