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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 …

Bor du eller jobber du i #Norge?️ StŸtter du ideen om at arbeidere selv skal forvalte sine arbeidsplasser, bygge på solidaritet, praktisere gjensidig stŸtte og organisere seg som likestilte uten hierarkier?
Opplever du urettferdig behandling på arbeidsplassen, konflikter i hverdagen eller en sjef som bruker makten sin mot deg? Ser du at kapitalisme, hierarki og partipolitikk aldri vil kun…

Analog Over-the-Air Federated Learning with Interference-Based Energy Harvesting
Ahmad Massud Tota Khel, Aissa Ikhlef, Zhiguo Ding, Hongjian Sun
https://arxiv.org/abs/2509.10123

Analog Over-the-Air Federated Learning with Interference-Based Energy Harvesting
We consider analog over-the-air federated learning, where devices harvest energy from in-band and out-band radio frequency signals, with the former also causing co-channel interference (CCI). To mitigate the aggregation error, we propose an effective denoising policy that does not require channel state information (CSI). We also propose an adaptive scheduling algorithm that dynamically adjusts the number of local training epochs based on available energy, enhancing device participation and lear…

FedDAF: Federated Domain Adaptation Using Model Functional Distance
Mrinmay Sen, Ankita Das, Sidhant Nair, C Krishna Mohan
https://arxiv.org/abs/2509.11819 https://

FedDAF: Federated Domain Adaptation Using Model Functional Distance
Federated Domain Adaptation (FDA) is a federated learning (FL) approach that improves model performance at the target client by collaborating with source clients while preserving data privacy. FDA faces two primary challenges: domain shifts between source and target data and limited labeled data at the target. Most existing FDA methods focus on domain shifts, assuming ample target data, yet often neglect the combined challenges of both domain shifts and data scarcity. Moreover, approaches that …

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…

Replaced article(s) found for cs.DC. https://arxiv.org/list/cs.DC/new
[1/1]:
- Bridging Memory Gaps: Scaling Federated Learning for Heterogeneous Clients
Yebo Wu, Jingguang Li, Chunlin Tian, Kahou Tam, Li Li, Chengzhong Xu

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…

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.

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…

Crosslisted article(s) found for cs.DC. https://arxiv.org/list/cs.DC/new
[1/1]:
- FedQS: Optimizing Gradient and Model Aggregation for Semi-Asynchronous Federated Learning
Yunbo Li, Jiaping Gui, Zhihang Deng, Fanchao Meng, Yue Wu