Tootfinder

Federato, which provides AI-powered insurance lifecycle software, raised a $100M Series D led by Goldman Sachs Alternatives, taking its total funding to $180M (InsurTech Analyst)
https://insurtechanalyst.com/2025/11/18/federato-launches-ai-n…

Building a big bribe room to sell the country off for parts while the government is rapidly deconstructed into a legislatively induced coma #uspol

Federated Learning in the Wild: A Comparative Study for Cybersecurity under Non-IID and Unbalanced Settings
Roberto Doriguzzi-Corin, Petr Sabel, Silvio Cretti, Silvio Ranise
https://arxiv.org/abs/2509.17836

Federated Learning in the Wild: A Comparative Study for Cybersecurity under Non-IID and Unbalanced Settings
Machine Learning (ML) techniques have shown strong potential for network traffic analysis; however, their effectiveness depends on access to representative, up-to-date datasets, which is limited in cybersecurity due to privacy and data-sharing restrictions. To address this challenge, Federated Learning (FL) has recently emerged as a novel paradigm that enables collaborative training of ML models across multiple clients while ensuring that sensitive data remains local. Nevertheless, Federated Av…

TACTFL: Temporal Contrastive Training for Multi-modal Federated Learning with Similarity-guided Model Aggregation
Guanxiong Sun, Majid Mirmehdi, Zahraa Abdallah, Raul Santos-Rodriguez, Ian Craddock, Telmo de Menezes e Silva Filho
https://arxiv.org/abs/2509.17532

TACTFL: Temporal Contrastive Training for Multi-modal Federated Learning with Similarity-guided Model Aggregation
Real-world federated learning faces two key challenges: limited access to labelled data and the presence of heterogeneous multi-modal inputs. This paper proposes TACTFL, a unified framework for semi-supervised multi-modal federated learning. TACTFL introduces a modality-agnostic temporal contrastive training scheme that conducts representation learning from unlabelled client data by leveraging temporal alignment across modalities. However, as clients perform self-supervised training on heteroge…

On Website Technicals (2018-12) - Tech updates: feeds, IMG beyond AMP, Gallery CMS, test cases, random rebuild order, speakable structured data, lighter 404, moar AMPy, featured snippet. - https://www.earth.org.uk/note-on-site-technicals-19.html

On Website Technicals (2018-12)
Tech updates: feeds, IMG beyond AMP, Gallery CMS, test cases, random rebuild order, speakable structured data, lighter 404, moar AMPy, featured snippet.

I need a way to invite a user on #Threads to federate. I can imagine a process similar to how #bridgyfed does it: an account over there that sends invitations to users on demand from here, accompanied with a message explaining how to enable federation and the benefits of doing it. There are a number of technica…

Building a big bribe room to sell the country off for parts while the government is rapidly deconstructed into a legislatively induced coma #uspol

orb-QFL: Orbital Quantum Federated Learning
Dev Gurung, Shiva Raj Pokhrel
https://arxiv.org/abs/2509.16505 https://arxiv.org/pdf/2509.16505

orb-QFL: Orbital Quantum Federated Learning
Recent breakthroughs in quantum computing present transformative opportunities for advancing Federated Learning (FL), particularly in non-terrestrial environments characterized by stringent communication and coordination constraints. In this study, we propose orbital QFL, termed orb-QFL, a novel quantum-assisted Federated Learning framework tailored for Low Earth Orbit (LEO) satellite constellations. Distinct from conventional FL paradigms, termed orb-QFL operates without centralized servers or…

Hybrid Reputation Aggregation: A Robust Defense Mechanism for Adversarial Federated Learning in 5G and Edge Network Environments
Saeid Sheikhi, Panos Kostakos, Lauri Loven
https://arxiv.org/abs/2509.18044

Hybrid Reputation Aggregation: A Robust Defense Mechanism for Adversarial Federated Learning in 5G and Edge Network Environments
Federated Learning (FL) in 5G and edge network environments face severe security threats from adversarial clients. Malicious participants can perform label flipping, inject backdoor triggers, or launch Sybil attacks to corrupt the global model. This paper introduces Hybrid Reputation Aggregation (HRA), a novel robust aggregation mechanism designed to defend against diverse adversarial behaviors in FL without prior knowledge of the attack type. HRA combines geometric anomaly detection with momen…

sat-QFL: Secure Quantum Federated Learning for Low Orbit Satellites
Dev Gurung, Shiva Raj Pokhrel
https://arxiv.org/abs/2509.16504 https://arxiv.org/pdf/25…

sat-QFL: Secure Quantum Federated Learning for Low Orbit Satellites
Low Earth orbit (LEO) constellations violate core assumptions of standard (quantum) federated learning (FL): client-server connectivity is intermittent, participation is time varying, and latency budgets are strict. We present sat-QFL, a hierarchical, access aware quantum federated learning (QFL) framework that partitions satellites into primary (ground connected) and secondary as inter-satellite links (ISL-only) roles, and schedules sequential, simultaneous, or asynchronous edge training align…