mlsanityData to 7 Oct 2026

Federated Learning Priorities Under the European Union Artificial Intelligence Act

arXiv 2402.05968 ↗First posted 5 Feb 2024cs.LG cs.AI cs.CYcited by 13

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Abstract (arXiv)

The age of AI regulation is upon us, with the European Union Artificial Intelligence Act (AI Act) leading the way. Our key inquiry is how this will affect Federated Learning (FL), whose starting point of prioritizing data privacy while performing ML fundamentally differs from that of centralized learning. We believe the AI Act and future regulations could be the missing catalyst that pushes FL toward mainstream adoption. However, this can only occur if the FL community reprioritizes its research focus. In our position paper, we perform a first-of-its-kind interdisciplinary analysis (legal and ML) of the impact the AI Act may have on FL and make a series of observations supporting our primary position through quantitative and qualitative analysis. We explore data governance issues and the concern for privacy. We establish new challenges regarding performance and energy efficiency within lifecycle monitoring. Taken together, our analysis suggests there is a sizable opportunity for FL to become a crucial component of AI Act-compliant ML systems and for the new regulation to drive the adoption of FL techniques in general. Most noteworthy are the opportunities to defend against data bias and enhance private and secure computation

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 33 of its 71 references that are in the map. First posted on arXiv 5 Feb 2024.

Builds on

33 of 71 references.

References · 33

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2022LAION-5B: An open large-scale dataset for training next generation image-text modelscited by 4,131
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  4. 2020The Pile: An 800GB Dataset of Diverse Text for Language Modelingcited by 2,346
  5. 2018Federated Learning with Non-IID Datacited by 1,099
  6. 2020Adaptive Federated Optimizationcited by 1,036
  7. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 707
  8. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 565
  9. 2020Learning under Concept Drift: A Reviewcited by 470
  10. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 449
  11. 2019Fair Resource Allocation in Federated Learningcited by 389
  12. 2015Deep Learning with Limited Numerical Precisioncited by 368
21 more
  1. 2020Amnesiac Machine Learningcited by 305
  2. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 298
  3. 2023Towards Unbounded Machine Unlearningcited by 261
  4. 2019Differentially Private Learning with Adaptive Clippingcited by 218
  5. 2021Evaluating Gradient Inversion Attacks and Defenses in Federated Learningcited by 196
  6. 2021Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspectivecited by 116
  7. 2023A Comprehensive Survey of Dataset Distillationcited by 114
  8. 2020Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspectivecited by 102
  9. 2023SLoRA: Federated Parameter Efficient Fine-Tuning of Language Modelscited by 92
  10. 2022Federated Unlearning: How to Efficiently Erase a Client in FL?cited by 90
  11. 2022FedPrompt: Communication-Efficient and Privacy Preserving Prompt Tuning in Federated Learningcited by 86
  12. 2020Privacy-preserving Traffic Flow Prediction: A Federated Learning Approachcited by 82
  13. 2020Can Federated Learning Save The Planet?cited by 52
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  18. 2023FedZero: Leveraging Renewable Excess Energy in Federated Learningcited by 11
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  20. 2023Federated Learning for Water Consumption Forecasting in Smart Citiescited by 7
  21. 2016Human Behaviour as an aspect of Cyber Security Assurancecited by 1

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Citing papers per year: 2024: 8; 2025: 5.

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  1. 2024The Federation Strikes Back: A Survey of Federated Learning Privacy Attacks, Defenses, Applications, and Policy Landscapecited by 13
  2. 2024Worldwide Federated Training of Language Modelscited by 5
  3. 2024How Data Inter-connectivity Shapes LLMs Unlearning: A Structural Unlearning Perspectivecited by 5
  4. 2025FedMentor: Domain-Aware Differential Privacy for Heterogeneous Federated LLMs in Mental Healthcited by 5
  5. 2024DEPT: Decoupled Embeddings for Pre-training Language Modelscited by 4
  6. 2024Compliance Cards: Automated EU AI Act Compliance Analyses amidst a Complex AI Supply Chaincited by 3
  7. 2025Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practicecited by 3
  8. 2024Combining Federated Learning and Control: A Surveycited by 2
  9. 2025Federated Learning from Molecules to Processes: A Perspectivecited by 2
  10. 2024Federated Learning and AI Regulation in the European Union: Who is Responsible? -- An Interdisciplinary Analysiscited by 1
  11. 2024Vision Paper: Designing Graph Neural Networks in Compliance with the European Artificial Intelligence Actcited by 0
  12. 2025Federated Learning for Data-Driven Feedforward Control: A Case Study on Vehicle Lateral Dynamicscited by 0
1 more
  1. 2025Bayesian Robust Aggregation for Federated Learningcited by 0