Federated Learning for Water Consumption Forecasting in Smart Cities

• URL: http://arxiv.org/abs/2301.13036v1
• Date: Mon, 30 Jan 2023 16:26:25 GMT
• Title: Federated Learning for Water Consumption Forecasting in Smart Cities
• Authors: Mohammed El Hanjri, Hibatallah Kabbaj, Abdellatif Kobbane, Amine Abouaomar
• Abstract summary: Water consumption remains a major concern among the world's future challenges. Deep learning models are trained using enormous volumes of consumption data in smart cities. This paper introduces a novel model for water consumption prediction in smart cities.
• Score: 3.4716081340827007
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Water consumption remains a major concern among the world's future challenges. For applications like load monitoring and demand response, deep learning models are trained using enormous volumes of consumption data in smart cities. On the one hand, the information used is private. For instance, the precise information gathered by a smart meter that is a part of the system's IoT architecture at a consumer's residence may give details about the appliances and, consequently, the consumer's behavior at home. On the other hand, enormous data volumes with sufficient variation are needed for the deep learning models to be trained properly. This paper introduces a novel model for water consumption prediction in smart cities while preserving privacy regarding monthly consumption. The proposed approach leverages federated learning (FL) as a machine learning paradigm designed to train a machine learning model in a distributed manner while avoiding sharing the users data with a central training facility. In addition, this approach is promising to reduce the overhead utilization through decreasing the frequency of data transmission between the users and the central entity. Extensive simulation illustrate that the proposed approach shows an enhancement in predicting water consumption for different households.

Related papers

• MetaTrading: An Immersion-Aware Model Trading Framework for Vehicular Metaverse Services [94.61039892220037]
  We present a novel immersion-aware model trading framework that incentivizes metaverse users (MUs) to contribute learning models for augmented reality (AR) services in the vehicular metaverse. Considering dynamic network conditions and privacy concerns, we formulate the reward decisions of MSPs as a multi-agent Markov decision process. Experimental results demonstrate that the proposed framework can effectively provide higher-value models for object detection and classification in AR services on real AR-related vehicle datasets.
  arXiv  Detail & Related papers  (2024-10-25T16:20:46Z)
• Secure short-term load forecasting for smart grids with transformer-based federated learning [0.0]
  Electricity load forecasting is an essential task within smart grids to assist demand and supply balance. Fine-grained load profiles can expose users' electricity consumption behaviors, which raises privacy and security concerns. This paper presents a novel transformer-based deep learning approach with federated learning for short-term electricity load prediction.
  arXiv  Detail & Related papers  (2023-10-26T15:27:55Z)
• Adaptive Model Pruning and Personalization for Federated Learning over Wireless Networks [72.59891661768177]
  Federated learning (FL) enables distributed learning across edge devices while protecting data privacy. We consider a FL framework with partial model pruning and personalization to overcome these challenges. This framework splits the learning model into a global part with model pruning shared with all devices to learn data representations and a personalized part to be fine-tuned for a specific device.
  arXiv  Detail & Related papers  (2023-09-04T21:10:45Z)
• Non-intrusive Water Usage Classification Considering Limited Training Data [5.935761705025763]
  Smart metering of domestic water consumption to continuously monitor the usage of different appliances has been shown to have an impact on people's behavior towards water conservation. The installation of multiple sensors to monitor each appliance currently has a high initial cost and as a result, monitoring consumption from different appliances using sensors is not cost-effective. We propose a new algorithm for classifying single and overlapping household water usage events, using the total domestic consumption measurements.
  arXiv  Detail & Related papers  (2023-01-02T11:57:36Z)
• Online Data Selection for Federated Learning with Limited Storage [53.46789303416799]
  Federated Learning (FL) has been proposed to achieve distributed machine learning among networked devices. The impact of on-device storage on the performance of FL is still not explored. In this work, we take the first step to consider the online data selection for FL with limited on-device storage.
  arXiv  Detail & Related papers  (2022-09-01T03:27:33Z)
• FedKD: Communication Efficient Federated Learning via Knowledge Distillation [56.886414139084216]
  Federated learning is widely used to learn intelligent models from decentralized data. In federated learning, clients need to communicate their local model updates in each iteration of model learning. We propose a communication efficient federated learning method based on knowledge distillation.
  arXiv  Detail & Related papers  (2021-08-30T15:39:54Z)
• DER Forecast using Privacy Preserving Federated Learning [0.0]
  A distributed machine learning approach, Federated Learning, is proposed to carry out DER forecasting using a network of IoT nodes. We consider a simulation study which includes 1000 DERs, and show that our method leads to an accurate prediction of preserve consumer privacy.
  arXiv  Detail & Related papers  (2021-07-07T14:25:43Z)
• Using Data Assimilation to Train a Hybrid Forecast System that Combines Machine-Learning and Knowledge-Based Components [52.77024349608834]
  We consider the problem of data-assisted forecasting of chaotic dynamical systems when the available data is noisy partial measurements. We show that by using partial measurements of the state of the dynamical system, we can train a machine learning model to improve predictions made by an imperfect knowledge-based model.
  arXiv  Detail & Related papers  (2021-02-15T19:56:48Z)
• Privacy Enhancing Machine Learning via Removal of Unwanted Dependencies [21.97951347784442]
  This paper studies new variants of supervised and adversarial learning methods, which remove the sensitive information in the data before they are sent out for a particular application. The explored methods optimize privacy preserving feature mappings and predictive models simultaneously in an end-to-end fashion. Experimental results on mobile sensing and face datasets demonstrate that our models can successfully maintain the utility performances of predictive models while causing sensitive predictions to perform poorly.
  arXiv  Detail & Related papers  (2020-07-30T19:55:10Z)
• UVeQFed: Universal Vector Quantization for Federated Learning [179.06583469293386]
  Federated learning (FL) is an emerging approach to train such learning models without requiring the users to share their possibly private labeled data. In FL, each user trains its copy of the learning model locally. The server then collects the individual updates and aggregates them into a global model. We show that combining universal vector quantization methods with FL yields a decentralized training system in which the compression of the trained models induces only a minimum distortion.
  arXiv  Detail & Related papers  (2020-06-05T07:10:22Z)
• Evaluating the Communication Efficiency in Federated Learning Algorithms [3.713348568329249]
  Recently, in light of new privacy legislations in many countries, the concept of Federated Learning (FL) has been introduced. In FL, mobile users are empowered to learn a global model by aggregating their local models, without sharing the privacy-sensitive data. This raises the challenge of communication cost when implementing FL at large scale.
  arXiv  Detail & Related papers  (2020-04-06T15:31:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.