Cover Image

Federated Learning Priorities Under the European Union Artificial Intelligence Act

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Woisetschläger, Herbert, Erben, Alexander, Marino, Bill, Wang, Shiqiang, Lane, Nicholas D., Mayer, Ruben, Jacobsen, Hans-Arno
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence Computers and Society Distributed, Parallel, and Cluster Computing I.2; I.2.11; K.5
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

FLEdge: Benchmarking Federated Machine Learning Applications in Edge Computing Systems
by: Woisetschläger, Herbert, et al.
Published: (2023)

A Survey on Efficient Federated Learning Methods for Foundation Model Training
by: Woisetschläger, Herbert, et al.
Published: (2024)

Federated Fine-Tuning of LLMs on the Very Edge: The Good, the Bad, the Ugly
by: Woisetschläger, Herbert, et al.
Published: (2023)

Federated Learning and AI Regulation in the European Union: Who is Responsible? -- An Interdisciplinary Analysis
by: Woisetschläger, Herbert, et al.
Published: (2024)

How Can We Train Deep Learning Models Across Clouds and Continents? An Experimental Study
by: Erben, Alexander, et al.
Published: (2023)

SIGN: Schema-Induced Games for Naming
by: Zhang, Ryan, et al.
Published: (2025)

ADF-LoRA: Alternating Low-Rank Aggregation for Decentralized Federated Fine-Tuning
by: Wang, Xiaoyu, et al.
Published: (2025)

Asynchronous Byzantine Federated Learning
by: Cox, Bart, et al.
Published: (2024)

Training Diffusion Models with Federated Learning
by: de Goede, Matthijs, et al.
Published: (2024)

Aergia: Leveraging Heterogeneity in Federated Learning Systems
by: Cox, Bart, et al.
Published: (2022)

Parameterizing Federated Continual Learning for Reproducible Research
by: Cox, Bart, et al.
Published: (2024)

Asynchronous Multi-Server Federated Learning for Geo-Distributed Clients
by: Zuo, Yuncong, et al.
Published: (2024)

Towards Optimal Heterogeneous Client Sampling in Multi-Model Federated Learning
by: Zhang, Haoran, et al.
Published: (2025)

Hyper-parameter Optimization for Federated Learning with Step-wise Adaptive Mechanism
by: Saadati, Yasaman, et al.
Published: (2024)

DAGER: Exact Gradient Inversion for Large Language Models
by: Petrov, Ivo, et al.
Published: (2024)

Federated Learning for Traffic Flow Prediction with Synthetic Data Augmentation
by: Orozco, Fermin, et al.
Published: (2024)

Separating Intelligence from Execution: A Workflow Engine for the Model Context Protocol
by: Parmar, Abhinav Singh
Published: (2026)

AMP4EC: Adaptive Model Partitioning Framework for Efficient Deep Learning Inference in Edge Computing Environments
by: Zhang, Guilin, et al.
Published: (2025)

Rewarding Beliefs, Not Actions: Consistency-Guided Credit Assignment for Long-Horizon Agents
by: Tang, Wenjie, et al.
Published: (2026)

A Full Compression Pipeline for Green Federated Learning in Communication-Constrained Environments
by: Colybes, Elouan, et al.
Published: (2026)

Complementarity, Augmentation, or Substitutivity? The Impact of Generative Artificial Intelligence on the U.S. Federal Workforce
by: Resh, William G., et al.
Published: (2025)

Agentic Compilation: Mitigating the LLM Rerun Crisis for Minimized-Inference-Cost Web Automation
by: Chundru, Jagadeesh
Published: (2026)

Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks
by: Telila, Yohannis Kifle, et al.
Published: (2025)

Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis
by: Lan, Guangchen, et al.
Published: (2024)

SI-ChainFL: Shapley-Incentivized Secure Federated Learning for High-Speed Rail Data Sharing
by: Zhao, Mingjie, et al.
Published: (2026)

How Machine Learning-Data Driven Replication Strategies Enhance Fault Tolerance in Large-Scale Distributed Systems
by: Murimi, Almond Kiruthu
Published: (2025)

Federated Few-Shot Learning on Neuromorphic Hardware: An Empirical Study Across Physical Edge Nodes
by: Motta, Steven, et al.
Published: (2026)

Roadmap for Edge AI: A Dagstuhl Perspective
by: Ding, Aaron Yi, et al.
Published: (2021)

Connecting Large Language Model Agent to High Performance Computing Resource
by: Ma, Heng, et al.
Published: (2025)

Quantize Once, Train Fast: Allreduce-Compatible Compression with Provable Guarantees
by: Xin, Jihao, et al.
Published: (2023)

Comparison of Autoscaling Frameworks for Containerised Machine-Learning-Applications in a Local and Cloud Environment
by: Schroeder, Christian, et al.
Published: (2023)

Federated Learning with Differential Privacy
by: Banse, Adrien, et al.
Published: (2024)

Towards Building Private LLMs: Exploring Multi-Node Expert Parallelism on Apple Silicon for Mixture-of-Experts Large Language Model
by: Chen, Mu-Chi, et al.
Published: (2025)

A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning
by: Rosendal, Daan, et al.
Published: (2026)

MESS+: Dynamically Learned Inference-Time LLM Routing in Model Zoos with Service Level Guarantees
by: Woisetschläger, Herbert, et al.
Published: (2025)

CodeCRDT: Observation-Driven Coordination for Multi-Agent LLM Code Generation
by: Pugachev, Sergey
Published: (2025)

HFedATM: Hierarchical Federated Domain Generalization via Optimal Transport and Regularized Mean Aggregation
by: Nguyen, Thinh, et al.
Published: (2025)

Prima.cpp: Fast 30-70B LLM Inference on Heterogeneous and Low-Resource Home Clusters
by: Li, Zonghang, et al.
Published: (2025)

SPARK: Igniting Communication-Efficient Decentralized Learning via Stage-wise Projected NTK and Accelerated Regularization
by: Xia, Li
Published: (2025)

Training LLMs on HPC Systems: Best Practices from the OpenGPT-X Project
by: Penke, Carolin, et al.
Published: (2025)