Tootfinder

Jeg vet ikke hva alle andre mener, men jeg synes Norsk Syndikalistisk Forbund – Internasjonale Arbeider-Assosiasjon (NSF-IAA) er en veldig viktig og bra organisasjon. De bygger videre på anarkosyndikalistiske tradisjoner i Norge som går helt tilbake til 1916. NSF-IAA står for en kampvillig, medlemsstyrt og frigjŸrende arbeiderbevegelse som utfordrer både byråkrati, partipolitikk og kapitalmakt.
De gir et ekte alternativ til de tradisjonelle fagforeningene som ofte er preget av kompromi…

Towards Dynamic Urban Scene Synthesis: The Digital Twin Descriptor Service
Ioannis Tsampras, Georgios Stergiopoulos, Tanya Politi, Spyros Denazis
https://arxiv.org/abs/2509.11810

Towards Dynamic Urban Scene Synthesis: The Digital Twin Descriptor Service
Digital twins have been introduced as supporters to city operations, yet existing scene-descriptor formats and digital twin platforms often lack the integration, federation, and adaptable connectivity that urban environments demand. Modern digital twin platforms decouple data streams and representations into separate architectural planes, fusing them only at the visualization layer and limiting potential for simulation or further processing of the combined assets. At the same time, geometry-cen…

PubSub-VFL: Towards Efficient Two-Party Split Learning in Heterogeneous Environments via Publisher/Subscriber Architecture
Yi Liu, Yang Liu, Leqian Zheng, Jue Hong, Junjie Shi, Qingyou Yang, Ye Wu, Cong Wang
https://arxiv.org/abs/2510.12494

PubSub-VFL: Towards Efficient Two-Party Split Learning in Heterogeneous Environments via Publisher/Subscriber Architecture
With the rapid advancement of the digital economy, data collaboration between organizations has become a well-established business model, driving the growth of various industries. However, privacy concerns make direct data sharing impractical. To address this, Two-Party Split Learning (a.k.a. Vertical Federated Learning (VFL)) has emerged as a promising solution for secure collaborative learning. Despite its advantages, this architecture still suffers from low computational resource utilization…

Byråkratiet, partipolitikken og de tradisjonelle fagforeningene er ikke bygd for kamp. De fungerer som serviceorganisasjoner underlagt staten og kapitalen, og undergraver dermed arbeiderklassens egen kampvilje.
Kampen for reell frigjŸring og selvstyre må derfor drives av arbeiderne selv gjennom direkte, solidariske aksjoner uten bindinger til politiske partier og formelle fredsplikter. Bare en fri og selvstyrt fagbevegelse kan utfordre de Ÿkonomiske elitene og skape et ekte arbeidsfol…

Towards Communication-Efficient Decentralized Federated Graph Learning over Non-IID Data
Shilong Wang, Jianchun Liu, Hongli Xu, Chenxia Tang, Qianpiao Ma, Liusheng Huang
https://arxiv.org/abs/2509.08409

Towards Communication-Efficient Decentralized Federated Graph Learning over Non-IID Data
Decentralized Federated Graph Learning (DFGL) overcomes potential bottlenecks of the parameter server in FGL by establishing a peer-to-peer (P2P) communication network among workers. However, while extensive cross-worker communication of graph node embeddings is crucial for DFGL training, it introduces substantial communication costs. Most existing works typically construct sparse network topologies or utilize graph neighbor sampling methods to alleviate the communication overhead in DFGL. Intu…

I think he is trying to say he is an Avenger without realising he is the supervillain.
Also, does he know that while he is torrenting the world libraries into his bank account, you could set up for $10 a month your own Mastodon instance which is ten times better federated than his 'federated' Threads?

Für mich haben sie was von Swinging Sixties - damit die geometrischen Op Art-Formen trotzdem nicht zu streng wirken, habe ich handgemalte abstrakte #wandklexschmuck Motive eingesetzt.
Zu finden auf #artjewellery #fediArt #mastoArt #creativeToots #wearableArt

Beyond Static Knowledge Messengers: Towards Adaptive, Fair, and Scalable Federated Learning for Medical AI
Jahidul Arafat, Fariha Tasmin, Sanjaya Poudel, Ahsan Habib Tareq, Iftekhar Haider
https://arxiv.org/abs/2510.06259

Beyond Static Knowledge Messengers: Towards Adaptive, Fair, and Scalable Federated Learning for Medical AI
Medical AI faces challenges in privacy-preserving collaborative learning while ensuring fairness across heterogeneous healthcare institutions. Current federated learning approaches suffer from static architectures, slow convergence (45-73 rounds), fairness gaps marginalizing smaller institutions, and scalability constraints (15-client limit). We propose Adaptive Fair Federated Learning (AFFL) through three innovations: (1) Adaptive Knowledge Messengers dynamically scaling capacity based on hete…

Series D, Episode 05 - Animals
CAPTAIN: There is certainly nothing down here about the work they were doing.
SERVALAN: And there won't be anything anywhere else, but they were there on Bucol Two towards the end of the war and they spent twenty million credits on whatever it was.
https://blake.torpidity.net/m/405/113…

Towards Carbon-Aware Container Orchestration: Predicting Workload Energy Consumption with Federated Learning
Zainab Saad, Jialin Yang, Henry Leung, Steve Drew
https://arxiv.org/abs/2510.03970

Towards Carbon-Aware Container Orchestration: Predicting Workload Energy Consumption with Federated Learning
The growing reliance on large-scale data centers to run resource-intensive workloads has significantly increased the global carbon footprint, underscoring the need for sustainable computing solutions. While container orchestration platforms like Kubernetes help optimize workload scheduling to reduce carbon emissions, existing methods often depend on centralized machine learning models that raise privacy concerns and struggle to generalize across diverse environments. In this paper, we propose a…