JV
J.R. Vega Sanchez
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Time-Series Forecasting with Hybrid Federated Learning
A Personalized Approach to Collaboration
Collaborative efforts in Predictive Maintenance and Control can be beneficial for manufacturers and customers in industrial environments. However, these efforts are challenged by the need for multi-dimensional sharing of information about the same type (horizontal) and piece (vertical) of equipment, privacy restrictions and the presence of heterogeneous data distributions across participants.
Existing solutions have addressed some of these challenges for forecasting or different purposes but there lacks a comprehensive approach that handles all of them for time series forecasting. To solve this problem, we introduce Time-series-based Personalized Hybrid Federated Learning (TPHFL), a hybrid federated learning (FL) strategy that combines Horizontal FL and Vertical FL to enable multi-level knowledge exchange while preserving data privacy. All participants use a personalization mechanism to make predictions that better suit their underlying data distribution.
Our approach employs a distributed model to handle vertical privacy constraints and addresses data heterogeneity across equipment through a personalisation mechanism. Through extensive experiments on four public and one industry-specific datasets, we show that TPHFL outperforms independent learning scenarios by 27.2%, providing a strong incentive for parties to collaborate.
We demonstrate the effectiveness of personalisation by showing an accuracy improvement of up to 42.7% when comparing TPHFL with personalisation to TPHFL without personalisation, and 32.7% when comparing traditional HFL methods to HFL with personalisation. Additionally, we evaluate a different configuration for personalisation and perform a detailed hyperparameter analysis to better understand the behaviour of TPHFL in different contexts. ...
Existing solutions have addressed some of these challenges for forecasting or different purposes but there lacks a comprehensive approach that handles all of them for time series forecasting. To solve this problem, we introduce Time-series-based Personalized Hybrid Federated Learning (TPHFL), a hybrid federated learning (FL) strategy that combines Horizontal FL and Vertical FL to enable multi-level knowledge exchange while preserving data privacy. All participants use a personalization mechanism to make predictions that better suit their underlying data distribution.
Our approach employs a distributed model to handle vertical privacy constraints and addresses data heterogeneity across equipment through a personalisation mechanism. Through extensive experiments on four public and one industry-specific datasets, we show that TPHFL outperforms independent learning scenarios by 27.2%, providing a strong incentive for parties to collaborate.
We demonstrate the effectiveness of personalisation by showing an accuracy improvement of up to 42.7% when comparing TPHFL with personalisation to TPHFL without personalisation, and 32.7% when comparing traditional HFL methods to HFL with personalisation. Additionally, we evaluate a different configuration for personalisation and perform a detailed hyperparameter analysis to better understand the behaviour of TPHFL in different contexts. ...
Collaborative efforts in Predictive Maintenance and Control can be beneficial for manufacturers and customers in industrial environments. However, these efforts are challenged by the need for multi-dimensional sharing of information about the same type (horizontal) and piece (vertical) of equipment, privacy restrictions and the presence of heterogeneous data distributions across participants.
Existing solutions have addressed some of these challenges for forecasting or different purposes but there lacks a comprehensive approach that handles all of them for time series forecasting. To solve this problem, we introduce Time-series-based Personalized Hybrid Federated Learning (TPHFL), a hybrid federated learning (FL) strategy that combines Horizontal FL and Vertical FL to enable multi-level knowledge exchange while preserving data privacy. All participants use a personalization mechanism to make predictions that better suit their underlying data distribution.
Our approach employs a distributed model to handle vertical privacy constraints and addresses data heterogeneity across equipment through a personalisation mechanism. Through extensive experiments on four public and one industry-specific datasets, we show that TPHFL outperforms independent learning scenarios by 27.2%, providing a strong incentive for parties to collaborate.
We demonstrate the effectiveness of personalisation by showing an accuracy improvement of up to 42.7% when comparing TPHFL with personalisation to TPHFL without personalisation, and 32.7% when comparing traditional HFL methods to HFL with personalisation. Additionally, we evaluate a different configuration for personalisation and perform a detailed hyperparameter analysis to better understand the behaviour of TPHFL in different contexts.
Existing solutions have addressed some of these challenges for forecasting or different purposes but there lacks a comprehensive approach that handles all of them for time series forecasting. To solve this problem, we introduce Time-series-based Personalized Hybrid Federated Learning (TPHFL), a hybrid federated learning (FL) strategy that combines Horizontal FL and Vertical FL to enable multi-level knowledge exchange while preserving data privacy. All participants use a personalization mechanism to make predictions that better suit their underlying data distribution.
Our approach employs a distributed model to handle vertical privacy constraints and addresses data heterogeneity across equipment through a personalisation mechanism. Through extensive experiments on four public and one industry-specific datasets, we show that TPHFL outperforms independent learning scenarios by 27.2%, providing a strong incentive for parties to collaborate.
We demonstrate the effectiveness of personalisation by showing an accuracy improvement of up to 42.7% when comparing TPHFL with personalisation to TPHFL without personalisation, and 32.7% when comparing traditional HFL methods to HFL with personalisation. Additionally, we evaluate a different configuration for personalisation and perform a detailed hyperparameter analysis to better understand the behaviour of TPHFL in different contexts.
The increasing demand for sharing Internet of Things (IoT) increases demand for a secure way of sharing data. Smart contracts could provide this because of its distributed nature. Proxy re-encryption is an encryption (PRE) method that can be used to share information. In this paper, a secure data sharing scheme is presented that uses multi-hop PRE with HyperLedger Fabric (HF). The scheme requires only two parties to participate with the data owner serving as the proxy and lets users remain access after re-encrypting the cipher. The implementation is used as means for demonstration and analysis. It still requires further work for actual deployment but demonstrates that the scheme holds in terms of efficiency and scalability.
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The increasing demand for sharing Internet of Things (IoT) increases demand for a secure way of sharing data. Smart contracts could provide this because of its distributed nature. Proxy re-encryption is an encryption (PRE) method that can be used to share information. In this paper, a secure data sharing scheme is presented that uses multi-hop PRE with HyperLedger Fabric (HF). The scheme requires only two parties to participate with the data owner serving as the proxy and lets users remain access after re-encrypting the cipher. The implementation is used as means for demonstration and analysis. It still requires further work for actual deployment but demonstrates that the scheme holds in terms of efficiency and scalability.