118 citations · 234 across the 11 of their papers we have counts for
11 papers
FedAli: Personalized Federated Learning Alignment with Prototype Layers for Generalized Mobile Services
Sannara Ek, Kaile Wang, François Portet +2
Personalized Federated Learning (PFL) enables distributed training on edge devices, allowing models to collaboratively learn global patterns while tailoring their parameters to bet…
Comparing Self-Supervised Learning Techniques for Wearable Human Activity Recognition
Sannara Ek, Riccardo Presotto, Gabriele Civitarese +3
Human Activity Recognition (HAR) based on the sensors of mobile/wearable devices aims to detect the physical activities performed by humans in their daily lives. Although supervise…
Combining Public Human Activity Recognition Datasets to Mitigate Labeled Data Scarcity
Riccardo Presotto, Sannara Ek, Gabriele Civitarese +3
The use of supervised learning for Human Activity Recognition (HAR) on mobile devices leads to strong classification performances. Such an approach, however, requires large amounts…
Evaluation of Regularization-based Continual Learning Approaches: Application to HAR
Bonpagna Kann, Sandra Castellanos-Paez, Philippe Lalanda
Pervasive computing allows the provision of services in many important areas, including the relevant and dynamic field of health and well-being. In this domain, Human Activity Reco…
Evaluation and comparison of federated learning algorithms for Human Activity Recognition on smartphones
Sannara Ek, François Portet, Philippe Lalanda +1
Pervasive computing promotes the integration of smart devices in our living spaces to develop services providing assistance to people. Such smart devices are increasingly relying o…
Transformer-based Models to Deal with Heterogeneous Environments in Human Activity Recognition
Sannara EK, François Portet, Philippe Lalanda
Human Activity Recognition (HAR) on mobile devices has been demonstrated to be possible using neural models trained on data collected from the device's inertial measurement units.…
Federated Self-Supervised Learning in Heterogeneous Settings: Limits of a Baseline Approach on HAR
Sannara Ek, Romain Rombourg, François Portet +1
Federated Learning is a new machine learning paradigm dealing with distributed model learning on independent devices. One of the many advantages of federated learning is that train…
Federated Continual Learning through distillation in pervasive computing
Anastasiia Usmanova, François Portet, Philippe Lalanda +1
Federated Learning has been introduced as a new machine learning paradigm enhancing the use of local devices. At a server level, FL regularly aggregates models learned locally on d…
Federated Learning and catastrophic forgetting in pervasive computing: demonstration in HAR domain
Anastasiia Usmanova, François Portet, Philippe Lalanda +1
Federated Learning has been introduced as a new machine learning paradigm enhancing the use of local devices. At a server level, FL regularly aggregates models learned locally on d…
A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and Comparison
Sannara Ek, François Portet, Philippe Lalanda +1
Pervasive computing promotes the installation of connected devices in our living spaces in order to provide services. Two major developments have gained significant momentum recent…
A distillation-based approach integrating continual learning and federated learning for pervasive services
Anastasiia Usmanova, François Portet, Philippe Lalanda +1
Federated Learning, a new machine learning paradigm enhancing the use of edge devices, is receiving a lot of attention in the pervasive community to support the development of smar…