Tootfinder

Privacy-enhancing Sclera Segmentation Benchmarking Competition: SSBC 2025
Matej Vitek, Darian Toma\v{s}evi\'c, Abhijit Das, Sabari Nathan, G\"okhan \"Ozbulak, G\"ozde Ay\c{s}e Tataro\u{g}lu \"Ozbulak, Jean-Paul Calbimonte, Andr\'e Anjos, Hariohm Hemant Bhatt, Dhruv Dhirendra Premani, Jay Chaudhari, Caiyong Wang, Jian Jiang, Chi Zhang, Qi Zhang, Iyyakutti Iyappan Ganapathi, Syed Sadaf Ali, Divya Velayudan, Maregu Assefa, Naoufel Werghi, Zachary A. Daniels, Le…

Privacy-enhancing Sclera Segmentation Benchmarking Competition: SSBC 2025
This paper presents a summary of the 2025 Sclera Segmentation Benchmarking Competition (SSBC), which focused on the development of privacy-preserving sclera-segmentation models trained using synthetically generated ocular images. The goal of the competition was to evaluate how well models trained on synthetic data perform in comparison to those trained on real-world datasets. The competition featured two tracks: $(i)$ one relying solely on synthetic data for model development, and $(ii)$ one co…

Weekend #Plankton Factoid 🦠🦐
For a while, there has been concern for how the ocean's #ecosystem is being modeled. Too often it is oversimplified with #phytoplankon and

FedFlex: Federated Learning for Diverse Netflix Recommendations
Sven Lankester, Manel Slokom, Gustavo de Carvalho Bertoli, Matias Vizcaino, Emmanuelle Beauxis Aussalet, Laura Hollink
https://arxiv.org/abs/2507.21115

FedFlex: Federated Learning for Diverse Netflix Recommendations
Federated learning is a decentralized approach that enables collaborative model training across multiple devices while preserving data privacy. It has shown significant potential in various domains, including healthcare and personalized recommendation systems. However, most existing work on federated recommendation systems has focused primarily on improving accuracy, with limited attention to fairness and diversity. In this paper, we introduce FedFlex, a federated recommender system for Netflix…

Optimizing Federated Learning Configurations for MRI Prostate Segmentation and Cancer Detection: A Simulation Study
Ashkan Moradi, Fadila Zerka, Joeran S. Bosma, Mohammed R. S. Sunoqrot, Bendik S. Abrahamsen, Derya Yakar, Jeroen Geerdink, Henkjan Huisman, Tone Frost Bathen, Mattijs Elschot
https://arxiv.org/abs/2507.22790

Optimizing Federated Learning Configurations for MRI Prostate Segmentation and Cancer Detection: A Simulation Study
Purpose: To develop and optimize a federated learning (FL) framework across multiple clients for biparametric MRI prostate segmentation and clinically significant prostate cancer (csPCa) detection. Materials and Methods: A retrospective study was conducted using Flower FL to train a nnU-Net-based architecture for MRI prostate segmentation and csPCa detection, using data collected from January 2010 to August 2021. Model development included training and optimizing local epochs, federated rounds,…

Fault-Tolerant Decentralized Distributed Asynchronous Federated Learning with Adaptive Termination Detection
Phani Sahasra Akkinepally, Manaswini Piduguralla, Sushant Joshi, Sathya Peri, Sandeep Kulkarni
https://arxiv.org/abs/2509.02186

Fault-Tolerant Decentralized Distributed Asynchronous Federated Learning with Adaptive Termination Detection
Federated Learning (FL) facilitates collaborative model training across distributed clients while ensuring data privacy. Traditionally, FL relies on a centralized server to coordinate learning, which creates bottlenecks and a single point of failure. Decentralized FL architectures eliminate the need for a central server and can operate in either synchronous or asynchronous modes. Synchronous FL requires all clients to compute updates and wait for one another before aggregation, guaranteeing con…

From Research to Reality: Feasibility of Gradient Inversion Attacks in Federated Learning
Viktor Valadi, Mattias {\AA}kesson, Johan \"Ostman, Salman Toor, Andreas Hellander
https://arxiv.org/abs/2508.19819

From Research to Reality: Feasibility of Gradient Inversion Attacks in Federated Learning
Gradient inversion attacks have garnered attention for their ability to compromise privacy in federated learning. However, many studies consider attacks with the model in inference mode, where training-time behaviors like dropout are disabled and batch normalization relies on fixed statistics. In this work, we systematically analyze how architecture and training behavior affect vulnerability, including the first in-depth study of inference-mode clients, which we show dramatically simplifies inv…

Federated Learning on Riemannian Manifolds: A Gradient-Free Projection-Based Approach
Hongye Wang, Zhaoye Pan, Chang He, Jiaxiang Li, Bo Jiang
https://arxiv.org/abs/2507.22855 h…

Federated Learning on Riemannian Manifolds: A Gradient-Free Projection-Based Approach
Federated learning (FL) has emerged as a powerful paradigm for collaborative model training across distributed clients while preserving data privacy. However, existing FL algorithms predominantly focus on unconstrained optimization problems with exact gradient information, limiting its applicability in scenarios where only noisy function evaluations are accessible or where model parameters are constrained. To address these challenges, we propose a novel zeroth-order projection-based algorithm o…

You can watch the latest #SouthPark episode for free on the South Park website.
https://southpark.cc.com/video-clips/ft9rdy/south-park-relax-guy