february

content/_index.md

@@ -12,22 +12,17 @@ OptAzur organizes a monthly seminar in Nice and Sophia-Antipolis, which alternat
 
 ### Next talk
 
-Monday, January 15th, 2024 (LJAD, Nice)
+Monday, February 19th, 2024 (I3S, Sophia-Antipolis)
 
-14h - [Marco Lorenzi](https://marcolorenzi.github.io) (Inria)
+14h - [Juan Peypouquet](https://www.rug.nl/staff/j.g.peypouquet/?lang=en) (University of Groningen)
 
-**Federated Learning in Healthcare Applications: from Theory to Practice**
+**TBA**
 
-This talk will illustrate current advances in federated learning (FL) for sensitive applications, from a both theoretical and practical perspectives. 
-In spite of the wide interest in the federated learning paradigm, current applications to sensitive domains, such as healthcare, are still challenging due to the complexity in dealing with heterogeneous and complex data hosted in different hospitals, as well as to the practical difficulty of deploying federated architectures in the real world.
-I will first cover a novel theory for modeling the impact of clients heterogeneity on the convergence guarantees of federated learning [1, 2]. In particular, I will study the robustness and variability of federated learning to heterogeneous conditions, by introducing the notion of stochastic aggregation weights. The proposed framework allows to derive novel federated optimization schemes to maximise the representativity and minimize the variability of clients contributions across federated optimization rounds.
-I will also introduce a novel perspective to Federated Unlearning (FU), a novel FL problem aiming at providing theoretical guarantees on the removal of the contribution of a given client from a federated training procedure. Upon unlearning request from a given client, FU is based on the definition of criteria to identify the optimal FL iteration from which FL has to be reinitialized, along with randomized perturbation mechanism to provide unlearning guarantees [3]. 
-Finally, from the practical standpoint, the talk will introduce Fed-BioMed, a development initiative aiming at translating federated learning to healthcare applications [4]. Fed-BioMed tackles the challenges required to meet real-world translation, concerning FL security, scalability and interoperability. I will give an illustration of the interplay between methodological development and translational effort that characterise the development of the Fed-BioMed FL platform, and discuss our current effort in delivering FL in hospitals networks. 
-[1] Fraboni Y., Vidal R., Kameni L. and Lorenzi M. Clustered sampling: Low-variance and improved representativity for clients selection in federated learning., In International Conference on Machine Learning (pp. 3407-3416). PMLR. 
-[2] Fraboni Y., Vidal R., Kameni L. and Lorenzi M. A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates., Journal of Machine Learning Research (110):1-43, 2023.
-[3] Fraboni Y., Vidal R., Kameni L. and Lorenzi M. Sequential Informed Federated Unlearning: Efficient and Provable Client Unlearning in Federated Optimization., arXiv preprint arXiv:2211.11656.
-[4] Cremonesi F., et al.Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications, White paper, arXiv:2304.12012.
+15h15 - [Khazhgali Kozhasov](https://scholar.google.com/citations?user=cWl9pB0AAAAJ) (LJAD, Nice)
 
+**Completely monotone functions and applications**
+
+Complete monotonicity of a smooth function on a convex cone is a strong property given by infinitely many sign conditions on the directional derivatives of the function. I will discuss results and questions around this concept that are motivated by research in convex optimization (interior-point methods), algebraic statistics (exponential families) and real algebraic geometry (hyperbolic and nonnegative polynomials). The talk is based on joint works with M. Michalek, B. Sturmfels and J.-B. Lasserre.
 
 ### Previous talks
 
@@ -37,6 +32,7 @@ Titles and abstracts [here](/previous)
 - #2: [Gersende Fort](https://perso.math.univ-toulouse.fr/gfort/) (CNRS, Institut de Mathématiques de Toulouse) and [Samuel Vaiter](https://samuelvaiter.com) (CNRS, Laboratoire J. A. Dieudonné)
 - #3: [Massimiliano Pontil](https://www.iit.it/people-details/-/people/massimiliano-pontil) (Italian Institute of Technology and University College London) and [Mathieu Carrière](https://www-sop.inria.fr/members/Mathieu.Carriere/) (Inria)
 - #4: [Maurizio Filippone](https://www.eurecom.fr/~filippon/) (EURECOM) and [Yassine Laguel](https://yassine-laguel.github.io) (Laboratoire J. A. Dieudonné)
+- #5: [Marco Lorenzi](https://marcolorenzi.github.io) (Inria)
 
 ## Events
 

content/previous.md

@@ -1,5 +1,21 @@
 # Previous talks @OptAzur
 
+## OptAzur #5 -- Monday, January 15th, 2024 (LJAD, Nice)
+
+14h - [Marco Lorenzi](https://marcolorenzi.github.io) (Inria)
+
+**Federated Learning in Healthcare Applications: from Theory to Practice**
+
+This talk will illustrate current advances in federated learning (FL) for sensitive applications, from a both theoretical and practical perspectives. 
+In spite of the wide interest in the federated learning paradigm, current applications to sensitive domains, such as healthcare, are still challenging due to the complexity in dealing with heterogeneous and complex data hosted in different hospitals, as well as to the practical difficulty of deploying federated architectures in the real world.
+I will first cover a novel theory for modeling the impact of clients heterogeneity on the convergence guarantees of federated learning [1, 2]. In particular, I will study the robustness and variability of federated learning to heterogeneous conditions, by introducing the notion of stochastic aggregation weights. The proposed framework allows to derive novel federated optimization schemes to maximise the representativity and minimize the variability of clients contributions across federated optimization rounds.
+I will also introduce a novel perspective to Federated Unlearning (FU), a novel FL problem aiming at providing theoretical guarantees on the removal of the contribution of a given client from a federated training procedure. Upon unlearning request from a given client, FU is based on the definition of criteria to identify the optimal FL iteration from which FL has to be reinitialized, along with randomized perturbation mechanism to provide unlearning guarantees [3]. 
+Finally, from the practical standpoint, the talk will introduce Fed-BioMed, a development initiative aiming at translating federated learning to healthcare applications [4]. Fed-BioMed tackles the challenges required to meet real-world translation, concerning FL security, scalability and interoperability. I will give an illustration of the interplay between methodological development and translational effort that characterise the development of the Fed-BioMed FL platform, and discuss our current effort in delivering FL in hospitals networks. 
+[1] Fraboni Y., Vidal R., Kameni L. and Lorenzi M. Clustered sampling: Low-variance and improved representativity for clients selection in federated learning., In International Conference on Machine Learning (pp. 3407-3416). PMLR. 
+[2] Fraboni Y., Vidal R., Kameni L. and Lorenzi M. A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates., Journal of Machine Learning Research (110):1-43, 2023.
+[3] Fraboni Y., Vidal R., Kameni L. and Lorenzi M. Sequential Informed Federated Unlearning: Efficient and Provable Client Unlearning in Federated Optimization., arXiv preprint arXiv:2211.11656.
+[4] Cremonesi F., et al.Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications, White paper, arXiv:2304.12012.
+
 ## OptAzur #4 -- Monday, December 18th, 2023 (Sophia)
 
 14h - [Maurizio Filippone](https://www.eurecom.fr/~filippon/) (EURECOM)