Tootfinder

Remarkable that this article coincides with the phishing attack spam pummeling instances that uses fake age verification as its attack surface. https://techcrunch.com/2025/08/29/mastodon-says-it-doesnt-have-the-means-to-comply-with-age…

Mastodon says it doesn't 'have the means' to comply with age verification laws | TechCrunch
Decentralized social network Mastodon says it cannot comply with age verification laws, like in Mississippi and elsewhere, and says it's up to individual server owners to decide.

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…

Improving Convergence for Semi-Federated Learning: An Energy-Efficient Approach by Manipulating Over-the-Air Distortion
Jingheng Zheng, Hui Tian, Wanli Ni, Yang Tian, Ping Zhang
https://arxiv.org/abs/2506.21893

Improving Convergence for Semi-Federated Learning: An Energy-Efficient Approach by Manipulating Over-the-Air Distortion
In this paper, we propose a hybrid learning framework that combines federated and split learning, termed semi-federated learning (SemiFL), in which over-the-air computation is utilized for gradient aggregation. A key idea is to strategically adjust the learning rate by manipulating over-the-air distortion for improving SemiFL's convergence. Specifically, we intentionally amplify amplitude distortion to increase the learning rate in the non-stable region, thereby accelerating convergence and red…

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…

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Media editor: edit images and videos on your phone

Tackling Federated Unlearning as a Parameter Estimation Problem
Antonio Balordi, Lorenzo Manini, Fabio Stella, Alessio Merlo
https://arxiv.org/abs/2508.19065 https://

Tackling Federated Unlearning as a Parameter Estimation Problem
Privacy regulations require the erasure of data from deep learning models. This is a significant challenge that is amplified in Federated Learning, where data remains on clients, making full retraining or coordinated updates often infeasible. This work introduces an efficient Federated Unlearning framework based on information theory, modeling leakage as a parameter estimation problem. Our method uses second-order Hessian information to identify and selectively reset only the parameters most se…

Social Security Whistleblower: DOGE unlawfully stored vital Numident data on a virtual private cloud #GiftLink #DOGE #PrivacyAct

DOGE Put Critical Social Security Data at Risk, Whistle-Blower Says
DOGE team members uploaded a database with the personal information of hundreds of millions of Americans to a vulnerable cloud server, according to the agency’s chief data officer.

Error Analysis for Over-the-Air Federated Learning under Misaligned and Time-Varying Channels
Xiaoyan Ma, Shahryar Zehtabi, Taejoon Kim, Christopher G. Brinton
https://arxiv.org/abs/2508.20277

Error Analysis for Over-the-Air Federated Learning under Misaligned and Time-Varying Channels
This paper investigates an OFDM-based over-the-air federated learning (OTA-FL) system, where multiple mobile devices, e.g., unmanned aerial vehicles (UAVs), transmit local machine learning (ML) models to a central parameter server (PS) for global model aggregation. The high mobility of local devices results in imperfect channel estimation, leading to a misalignment problem, i.e., the model parameters transmitted from different local devices do not arrive at the central PS simultaneously. Moreov…

Federated Learning with Heterogeneous and Private Label Sets
Adam Breitholtz, Edvin Listo Zec, Fredrik D. Johansson
https://arxiv.org/abs/2508.18774 https://

Federated Learning with Heterogeneous and Private Label Sets
Although common in real-world applications, heterogeneous client label sets are rarely investigated in federated learning (FL). Furthermore, in the cases they are, clients are assumed to be willing to share their entire label sets with other clients. Federated learning with private label sets, shared only with the central server, adds further constraints on learning algorithms and is, in general, a more difficult problem to solve. In this work, we study the effects of label set heterogeneity on…

Age Verification is the new attack surface.