ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2402.05968
  4. Cited By
Federated Learning Priorities Under the European Union Artificial
  Intelligence Act

Federated Learning Priorities Under the European Union Artificial Intelligence Act

5 February 2024
Herbert Woisetschläger
Alexander Erben
Bill Marino
Shiqiang Wang
Nicholas D. Lane
R. Mayer
Hans-Arno Jacobsen
ArXivPDFHTML

Papers citing "Federated Learning Priorities Under the European Union Artificial Intelligence Act"

9 / 9 papers shown
Title
Bayesian Robust Aggregation for Federated Learning
Bayesian Robust Aggregation for Federated Learning
Aleksandr Karakulev
Usama Zafar
Salman Toor
Prashant Singh
FedML
23
0
0
05 May 2025
Federated Learning for Privacy-Preserving Feedforward Control in Multi-Agent Systems
Jakob Weber
Markus Gurtner
Benedikt Alt
Adrian Trachte
Andreas Kugi
62
0
0
04 Mar 2025
Vision Paper: Designing Graph Neural Networks in Compliance with the
  European Artificial Intelligence Act
Vision Paper: Designing Graph Neural Networks in Compliance with the European Artificial Intelligence Act
Barbara Hoffmann
Jana Vatter
R. Mayer
21
0
0
29 Oct 2024
DEPT: Decoupled Embeddings for Pre-training Language Models
DEPT: Decoupled Embeddings for Pre-training Language Models
Alex Iacob
Lorenzo Sani
Meghdad Kurmanji
William F. Shen
Xinchi Qiu
Dongqi Cai
Yan Gao
Nicholas D. Lane
VLM
43
0
0
07 Oct 2024
Combining Federated Learning and Control: A Survey
Combining Federated Learning and Control: A Survey
Jakob Weber
Markus Gurtner
A. Lobe
Adrian Trachte
Andreas Kugi
FedML
AI4CE
24
2
0
12 Jul 2024
Worldwide Federated Training of Language Models
Worldwide Federated Training of Language Models
Alexandru Iacob
Lorenzo Sani
Bill Marino
Preslav Aleksandrov
William F. Shen
Nicholas D. Lane
FedML
30
1
0
23 May 2024
Federated Fine-Tuning of LLMs on the Very Edge: The Good, the Bad, the
  Ugly
Federated Fine-Tuning of LLMs on the Very Edge: The Good, the Bad, the Ugly
Herbert Woisetschläger
Alexander Erben
Shiqiang Wang
R. Mayer
Hans-Arno Jacobsen
FedML
17
17
0
04 Oct 2023
FedBN: Federated Learning on Non-IID Features via Local Batch
  Normalization
FedBN: Federated Learning on Non-IID Features via Local Batch Normalization
Xiaoxiao Li
Meirui Jiang
Xiaofei Zhang
Michael Kamp
Qi Dou
OOD
FedML
163
770
0
15 Feb 2021
The Pile: An 800GB Dataset of Diverse Text for Language Modeling
The Pile: An 800GB Dataset of Diverse Text for Language Modeling
Leo Gao
Stella Biderman
Sid Black
Laurence Golding
Travis Hoppe
...
Horace He
Anish Thite
Noa Nabeshima
Shawn Presser
Connor Leahy
AIMat
236
1,508
0
31 Dec 2020
1