Federated Learning is a distributed learning solution for machine learning problems without the need of collecting the available data in a single centralized data centre. With the standard FL approaches, model training is performed locally and a centralized server collects and elaborates the trainable parameters of the local models: even if data are not shared, the presence of the centralized server still rises trust and security issues. In this work, we introduce the Decentralized Federated Learning (DECFEDAVG) algorithm, which aims at achieving complete decentralization by the lack of a coordination server, and compare its performance against the original federated learning algorithm Federated Averaging (FEDAVG) over the Nonintrusive Load Monitoring problem.
Decentralized Federated Learning for Nonintrusive Load Monitoring in Smart Energy Communities / Giuseppi, A.; Manfredi, S.; Menegatti, D.; Pietrabissa, A.; Poli, C.. - (2022), pp. 312-317. (Intervento presentato al convegno 30th Mediterranean Conference on Control and Automation, MED 2022 tenutosi a Athens; Greece) [10.1109/MED54222.2022.9837291].
Decentralized Federated Learning for Nonintrusive Load Monitoring in Smart Energy Communities
Giuseppi A.
;Menegatti D.;Pietrabissa A.;
2022
Abstract
Federated Learning is a distributed learning solution for machine learning problems without the need of collecting the available data in a single centralized data centre. With the standard FL approaches, model training is performed locally and a centralized server collects and elaborates the trainable parameters of the local models: even if data are not shared, the presence of the centralized server still rises trust and security issues. In this work, we introduce the Decentralized Federated Learning (DECFEDAVG) algorithm, which aims at achieving complete decentralization by the lack of a coordination server, and compare its performance against the original federated learning algorithm Federated Averaging (FEDAVG) over the Nonintrusive Load Monitoring problem.File | Dimensione | Formato | |
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Note: DOI: 10.1109/MED54222.2022.9837291
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