Tootfinder

That’s some free speech right there. #uspol

RE: https://federate.social/@mattblaze/115544955814466222
Talking about this yesterday but more generally. Asking for an option for a phone (say) to globally disable ALL optional features, so I can enable only those I want when I want and at my leisure. Would be the first thing to do on a new device or after an upgrade.

Same.
https://federate.social/@mattblaze/115718897611725214

Xu Bo, a Chinese billionaire, was seeking parental rights to at least four unborn children,
and the court’s additional research showed that he had already fathered or was in the process of fathering at least eight more
—all through surrogates.
When called in for a confidential hearing in the summer of 2023,
he never entered the courtroom, according to people who attended the hearing.
The maker of fantasy videogames lived in China and appeared via video, speaking …

China fertility facts of the day - Marginal REVOLUTION
A Chinese billionaire was seeking parental rights to at least four unborn children, and the court’s additional research showed that he had already fathered or was in the process of fathering at least eight more—all through surrogates. When Pellman called Xu Bo in for a confidential hearing in the summer of 2023, he never entered […]

Hierarchical Federated Learning for Crop Yield Prediction in Smart Agricultural Production Systems
Anas Abouaomar, Mohammed El hanjri, Abdellatif Kobbane, Anis Laouiti, Khalid Nafil
https://arxiv.org/abs/2510.12727

Hierarchical Federated Learning for Crop Yield Prediction in Smart Agricultural Production Systems
In this paper, we presents a novel hierarchical federated learning architecture specifically designed for smart agricultural production systems and crop yield prediction. Our approach introduces a seasonal subscription mechanism where farms join crop-specific clusters at the beginning of each agricultural season. The proposed three-layer architecture consists of individual smart farms at the client level, crop-specific aggregators at the middle layer, and a global model aggregator at the top le…

FLAMMABLE: A Multi-Model Federated Learning Framework with Multi-Model Engagement and Adaptive Batch Sizes
Shouxu Lin, Zimeng Pan, Yuhang Yao, Haeyoung Noh, Pei Zhang, Carlee Joe-Wong
https://arxiv.org/abs/2510.10380

FLAMMABLE: A Multi-Model Federated Learning Framework with Multi-Model Engagement and Adaptive Batch Sizes
Multi-Model Federated Learning (MMFL) is an emerging direction in Federated Learning (FL) where multiple models are trained in parallel, generally on various datasets. Optimizing the models' accuracies and training times in the MMFL setting requires adapting to data and system heterogeneity across clients as in single-model FL; these challenges are amplified in the MMFL setting due to additional heterogeneity across models. Neither existing solutions nor naïve extensions of single-model FL fra…

That’s some free speech right there. #uspol

Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practice
Kevin Kuo, Chhavi Yadav, Virginia Smith
https://arxiv.org/abs/2510.12595 https://

Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practice
Cross-silo federated learning (FL) is a promising approach to enable cross-organization collaboration in machine learning model development without directly sharing private data. Despite growing organizational interest driven by data protection regulations such as GDPR and HIPAA, the adoption of cross-silo FL remains limited in practice. In this paper, we conduct an interview study to understand the practical challenges associated with cross-silo FL adoption. With interviews spanning a diverse …