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This post is part of a series on privacy-preserving federated learning. The series is a collaboration between CDEI and the US National Institute of Standards and Technology (NIST). Learn more and read all the posts published to date on the …

This post is part of a series on privacy-preserving federated learning. The series is a collaboration between the Responsible Technology Adoption Unit (RTA) and the US National Institute of Standards and Technology (NIST). Learn more and read all the posts …

This post is part of a series on privacy-preserving federated learning. The series is a collaboration between the Responsible Technology Adoption Unit (RTA) and the US National Institute of Standards and Technology (NIST). Learn more and read all the posts …

International audience ; Despite impressive results, deep learning-based technologies also raise severe privacy and environmental concerns induced by the training procedure often conducted in data centers. In response, alternatives to centralized training such as Federated Learning (FL) have emerged. Perhaps unexpectedly, FL in particular is starting to be deployed at a global scale by companies that must adhere to new legal demands and policies originating from governments and the civil society for privacy protection. However, the potential environmental impact related to FL remains unclear and unexplored. This paper offers the first-ever systematic study of the carbon footprint of FL. First, we propose a rigorous model to quantify the carbon footprint, hence facilitating the investigation of the relationship between FL design and carbon emissions. Then, we compare the carbon footprint of FL to traditional centralized learning. Finally, we highlight and connect the reported results to the future challenges and trends in FL to reduce its environmental impact, including algorithms efficiency, hardware capabilities, and stronger industry transparency.

International audience ; Despite impressive results, deep learning-based technologies also raise severe privacy and environmental concerns induced by the training procedure often conducted in data centers. In response, alternatives to centralized training such as Federated Learning (FL) have emerged. Perhaps unexpectedly, FL in particular is starting to be deployed at a global scale by companies that must adhere to new legal demands and policies originating from governments and the civil society for privacy protection. However, the potential environmental impact related to FL remains unclear and unexplored. This paper offers the first-ever systematic study of the carbon footprint of FL. First, we propose a rigorous model to quantify the carbon footprint, hence facilitating the investigation of the relationship between FL design and carbon emissions. Then, we compare the carbon footprint of FL to traditional centralized learning. Finally, we highlight and connect the reported results to the future challenges and trends in FL to reduce its environmental impact, including algorithms efficiency, hardware capabilities, and stronger industry transparency.

In our second post we described attacks on models and the concepts of input privacy and output privacy. ln our previous post, we described horizontal and vertical partitioning of data in privacy-preserving federated learning (PPFL) systems. In this post, we …

International audience ; Preserving the privacy of mobility data has been the center of active research in the last decade as this data may reveal sensitive information about individuals (e.g., home, work places, political, religious, sexual preferences). In this context, a large variety of Location Privacy Protection Mechanisms (LPPMs) have been proposed. To employ LPPMs more effectively for the benefits of the users' privacy, adaptive solutions that dynamically combine LPPMs have also been investigated. These solutions apply various LPPMs on a given trace and choose the one that better meets privacy and utility requirements. To meet this objective, adaptive solutions often rely on a trusted proxy that gathers users' traces and apply LPPMs locally. In this paper we release this assumption by designing an approach, i.e., EDEN, where mobility data never leaves the user's device before being protected by an appropriate LPPM. Experimental evaluation performed on real mobility traces shows that EDEN with its local and adaptive strategy outperforms individual LPPMs both in terms of privacy and utility metrics.

International audience ; Preserving the privacy of mobility data has been the center of active research in the last decade as this data may reveal sensitive information about individuals (e.g., home, work places, political, religious, sexual preferences). In this context, a large variety of Location Privacy Protection Mechanisms (LPPMs) have been proposed. To employ LPPMs more effectively for the benefits of the users' privacy, adaptive solutions that dynamically combine LPPMs have also been investigated. These solutions apply various LPPMs on a given trace and choose the one that better meets privacy and utility requirements. To meet this objective, adaptive solutions often rely on a trusted proxy that gathers users' traces and apply LPPMs locally. In this paper we release this assumption by designing an approach, i.e., EDEN, where mobility data never leaves the user's device before being protected by an appropriate LPPM. Experimental evaluation performed on real mobility traces shows that EDEN with its local and adaptive strategy outperforms individual LPPMs both in terms of privacy and utility metrics.

International audience ; Preserving the privacy of mobility data has been the center of active research in the last decade as this data may reveal sensitive information about individuals (e.g., home, work places, political, religious, sexual preferences). In this context, a large variety of Location Privacy Protection Mechanisms (LPPMs) have been proposed. To employ LPPMs more effectively for the benefits of the users' privacy, adaptive solutions that dynamically combine LPPMs have also been investigated. These solutions apply various LPPMs on a given trace and choose the one that better meets privacy and utility requirements. To meet this objective, adaptive solutions often rely on a trusted proxy that gathers users' traces and apply LPPMs locally. In this paper we release this assumption by designing an approach, i.e., EDEN, where mobility data never leaves the user's device before being protected by an appropriate LPPM. Experimental evaluation performed on real mobility traces shows that EDEN with its local and adaptive strategy outperforms individual LPPMs both in terms of privacy and utility metrics.

International audience ; Preserving the privacy of mobility data has been the center of active research in the last decade as this data may reveal sensitive information about individuals (e.g., home, work places, political, religious, sexual preferences). In this context, a large variety of Location Privacy Protection Mechanisms (LPPMs) have been proposed. To employ LPPMs more effectively for the benefits of the users' privacy, adaptive solutions that dynamically combine LPPMs have also been investigated. These solutions apply various LPPMs on a given trace and choose the one that better meets privacy and utility requirements. To meet this objective, adaptive solutions often rely on a trusted proxy that gathers users' traces and apply LPPMs locally. In this paper we release this assumption by designing an approach, i.e., EDEN, where mobility data never leaves the user's device before being protected by an appropriate LPPM. Experimental evaluation performed on real mobility traces shows that EDEN with its local and adaptive strategy outperforms individual LPPMs both in terms of privacy and utility metrics.

International audience ; Preserving the privacy of mobility data has been the center of active research in the last decade as this data may reveal sensitive information about individuals (e.g., home, work places, political, religious, sexual preferences). In this context, a large variety of Location Privacy Protection Mechanisms (LPPMs) have been proposed. To employ LPPMs more effectively for the benefits of the users' privacy, adaptive solutions that dynamically combine LPPMs have also been investigated. These solutions apply various LPPMs on a given trace and choose the one that better meets privacy and utility requirements. To meet this objective, adaptive solutions often rely on a trusted proxy that gathers users' traces and apply LPPMs locally. In this paper we release this assumption by designing an approach, i.e., EDEN, where mobility data never leaves the user's device before being protected by an appropriate LPPM. Experimental evaluation performed on real mobility traces shows that EDEN with its local and adaptive strategy outperforms individual LPPMs both in terms of privacy and utility metrics.