mlsanityData to 7 Oct 2026

Agentic Federated Learning: The Future of Distributed Training Orchestration

arXiv 2604.04895 ↗First posted 6 Apr 2026cs.MA cs.AIcited by 0

On the map: LLM agents and tool use → Agentic AI for wireless networks

Abstract (arXiv)

Although Federated Learning (FL) promises privacy and distributed collaboration, its effectiveness in real-world scenarios is often hampered by the stochastic heterogeneity of clients and unpredictable system dynamics. Existing static optimization approaches fail to adapt to these fluctuations, resulting in resource underutilization and systemic bias. In this work, we propose a paradigm shift towards Agentic-FL, a framework where Language Model-based Agents (LMagents) assume autonomous orchestration roles. Unlike rigid protocols, we demonstrate how server-side agents can mitigate selection bias through contextual reasoning, while client-side agents act as local guardians, dynamically managing privacy budgets and adapting model complexity to hardware constraints. More than just resolving technical inefficiencies, this integration signals the evolution of FL towards decentralized ecosystems, where collaboration is negotiated autonomously, paving the way for future markets of incentive-based models and algorithmic justice. We discuss the reliability (hallucinations) and security challenges of this approach, outlining a roadmap for resilient multi-agent systems in federated environments.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Agentic AI for wireless networks, one of 16 directions in LLM agents and tool use. Builds on 32 of its 55 references that are in the map. First posted on arXiv 6 Apr 2026.

Builds on

32 of 55 references.

References · 32

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  10. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 665
  11. 2021Towards Personalized Federated Learningcited by 462
  12. 2025Small Language Models are the Future of Agentic AIcited by 254
20 more
  1. 2023Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessmentcited by 227
  2. 2023Towards Building the Federated GPT: Federated Instruction Tuningcited by 184
  3. 2024OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learningcited by 124
  4. 2021Asynchronous Federated Learning on Heterogeneous Devices: A Surveycited by 93
  5. 2025A Survey on Trustworthy LLM Agents: Threats and Countermeasurescited by 93
  6. 2019SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overheadcited by 82
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  8. 2022Client Selection in Federated Learning: Principles, Challenges, and Opportunitiescited by 62
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  10. 2021Privacy-preserving Federated Learning based on Multi-key Homomorphic Encryptioncited by 46
  11. 2024AGILE: A Novel Reinforcement Learning Framework of LLM Agentscited by 45
  12. 2023Federated Learning for Computationally-Constrained Heterogeneous Devices: A Surveycited by 40
  13. 2021Non-IID data and Continual Learning processes in Federated Learning: A long road aheadcited by 36
  14. 2024Proof of Thought : Neurosymbolic Program Synthesis allows Robust and Interpretable Reasoningcited by 17
  15. 2024Federated Learning in Practice: Reflections and Projectionscited by 15
  16. 2025Federated Continual Learning: Concepts, Challenges, and Solutionscited by 13
  17. 2024Efficient Federated Learning against Heterogeneous and Non-stationary Client Unavailabilitycited by 9
  18. 2024Personalized Federated Learning with Mixture of Models for Adaptive Prediction and Model Fine-Tuningcited by 8
  19. 2024Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and Analysiscited by 8
  20. 2024Adaptive Client Selection with Personalization for Communication Efficient Federated Learningcited by 1

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  2. 2026FL-MAESTRO: Multi-Agent LLM Orchestration for Resource-Constrained Federated Learning
  3. 2026Federated Agent Optimization
  4. 2025Fed-SE: Federated Self-Evolution for Privacy-Constrained Multi-Environment LLM Agents
  5. 2026FederatedSkill: Federated Learning for Agentic Skill Evolution
  6. 2025From Privacy to Trust in the Agentic Era: A Taxonomy of Challenges in Trustworthy Federated Learning Through the Lens of Trust Report 2.0
  7. 2026Learning to Collaborate: An Orchestrated-Decentralized Framework for Peer-to-Peer LLM Federation
  8. 2026Auto-FL-Research: Agentic Search for Federated Learning Algorithms