Federated learning for time series forecasting using LSTM networks: exploiting similarities through clustering

Díaz González, Fernando (2019). Federated learning for time series forecasting using LSTM networks: exploiting similarities through clustering. Thesis (Master thesis), E.T.S. de Ingenieros Informáticos (UPM).

Description

Title: Federated learning for time series forecasting using LSTM networks: exploiting similarities through clustering
Author/s:
  • Díaz González, Fernando
Contributor/s:
  • Boström, Henrik
  • Girdzijauskas, Šarūnas
Item Type: Thesis (Master thesis)
Masters title: Data Science
Date: 2019
Subjects:
Freetext Keywords: Federated learning; Time series forecasting; Clustering; Time series feature extraction; Recurrent neural networks; Long Short-Term Memory
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Otro
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Federated learning poses a statistical challenge when training on highly heterogeneous sequence data. For example, time-series telecom data collected over long intervals regularly shows mixed fluctuations and patterns. These distinct distributions are an inconvenience when a node not only plans to contribute to the creation of the global model but also plans to apply it on its local dataset. In this scenario, adopting a one-fits-all approach might be inadequate, even when using state-of-the-art machine learning techniques for time series forecasting, such as Long Short-Term Memory (LSTM) networks, which have proven to be able to capture many idiosyncrasies and generalise to new patterns. In this work, we show that by clustering the clients using these patterns and selectively aggregating their updates in different global models can improve local performance with minimal overhead, as we demonstrate through experiments using realworld time series datasets and a basic LSTM model.

More information

Item ID: 64239
DC Identifier: http://oa.upm.es/64239/
OAI Identifier: oai:oa.upm.es:64239
Deposited by: Biblioteca Facultad de Informatica
Deposited on: 01 Oct 2020 09:20
Last Modified: 01 Oct 2020 09:20
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