Federated Learning (FL) is a decentralized paradigm that enables a client-server architecture to collaboratively train a global Artificial Intelligence model without sharing raw data, thereby preserving privacy. A key challenge in FL is Non-IID data. Quantity Skew (QS) is a particular problem of Non-IID, where clients hold highly heterogeneous data volumes. Clustered Federated Learning (CFL) is an emergent variant of FL that presents a promising solution to Non-IID problem. It improves models' performance by grouping clients with similar data distributions into clusters. CFL methods generally fall into two operating strategies. In the first strategy, clients select the cluster that minimizes the local training loss. In the second strategy, the server groups clients based on local model similarities. However, most CFL methods lack systematic evaluation under QS but present significant challenges because of it.

Clustering is an important data mining task which is widely spread in various domains such as biology, finance, marketing, healthcare, and social sciences. It allows the end user to discover, through built clusters, relationships within data. Many non-expert users perceive clustering as an "easy" task because it always produces a result. However, choosing a clustering algorithm at random, without proper parameter tuning, often leads to poor results. In particular, an important choice when applying a clustering algorithm to a specific dataset is the similarity measure. Since clustering algorithms rely on similarities between data points to build clusters, the chosen similarity measure should fit the data as accurately as possible in order to form the best clusters. Mixed Data are data that are characterized by numerical as well as categorical attributes. When clustering mixed data, the same similarity measure cannot be used for the two attribute types. Commonly a pair of similarity measures is used, one dedicated to numerical attributes and one dedicated to categorical attributes. The choice of these two most appropriate similarity measures is very important in mixed data, as it significantly affects the clustering performance.

In many domains, we face heterogeneous data with both numeric and categorical attributes. Clustering such data is challenging because the notion of similarity is not well defined due to the multiple data types. Existing clustering algorithms for these data are mainly based on two strategies: the homogenization one where all attributes are converted to a single type and the mixed one where similarity measures for the different data types are combined to define a similarity measure for heterogeneous data. We propose a framework in which we evaluate and compare several clustering algorithms using these two strategies on many real-world data sets. Then, motivated by the importance of similarity in clustering and the diversity of similarity measures for each data type, we proposed as a second study, to evaluate how their choice affects the performance of clustering algorithms using the mixed strategy. Our results suggest that the mixed strategy is preferable to the homogenization one since it uses adapted similarity measures for the different data types. Furthermore, the choice of similarity measures is very important for most of used mixed methods and an optimal choice may lead to great improvements compared to classically used similarity measures.

International audience

Improving the egocentric network-based user's profile building process by taking into account the dynamic characteristics of social networks can be relevant in many applications. To achieve this aim, we propose to apply a time-aware method into an existing egocentric-based user profiling process, based on previous contributions of our team. The aim of this strategy is to weight user's interests according to their relevance and freshness. The time awareness weight of an interest is computed by combining the relevance of individuals in the user's egocentric network (computed by taking into account the freshness of their ties) with the information relevance (computed by taking into account its freshness). The experiments on scientific publications networks (DBLP/Mendeley) allow us to demonstrate the effectiveness of our proposition compared to the existing time-agnostic egocentric network-based user profiling process.

Nowadays, social networks are more and more widely used as a solution for enriching users' profiles in systems such as recommender systems or personalized systems. For an unknown user's interest, the user's social network can be a meaningful data source for deriving that interest. However, in the literature very few techniques are designed to meet this solution. Existing techniques usually focus on people individually selected in the user's social network, and strongly depend on each author's objective. To improve these techniques, we propose to use a community based algorithm that is applied to a part of the user's social network (egocentric network) and that can be reused for any purpose (e.g. personalization, recommendation). We compute weighted user's interests from these communities by considering their semantics (interests related to communities) and their structural measures (e.g. centrality measures) in the egocentric network graph. A first experiment conducted in Facebook demonstrates the usefulness of this technique compared to individuals based techniques, and the influence of structural measures (related to communities) on the quality of derived profiles. The results also raise the problem of users' privacy in platforms such as online social networks. To enable users to better protect their privacy, these platforms should provide their users with a way to also make their friendlist private.

In this paper, we are interested in the context of the French government's new efforts to put into practice the concept of the "personal health record (PHR)". We propose an ontology-based solution to improve the access to PHR and to extend it with the annotations that the patient can add and that will constitute the Annotated PHR. We consider that each user can manage the annotations access policies. We also develop an ontology-based solution for granting medical agents with useful, authorized and relevant information, which adopts XACML query rewriting mechanisms. The paper illustrates how the proposed framework could assist patients during their travels, when an unexpected health disorder takes place and the suitable specialist is not available.

Nowadays, online social networks host more and more applications in order to provide their users with the possibility of finding everything they need on a single platform. The number and diversity of interactions that take place over time between users and applications within these platforms make these environments very good candidates for learning various types of information about users’ interests. We are particularly interested in the determination of users’ short-term and long-term interests which are essential for adaptative systems that take into account the evolution of user’s needs. While studies in adaptative systems focus on computing interests’ weight value and time periods to determine user’s short-term and long-term profile, we focus instead on temporal graphs’ visualization of users’ interests. From a case study on Facebook, we use dynamic graphs in order to view the influence of social ties on the user’s interests.