mlsanityData to 14 Oct 2026

RAG-based User Profiling for Precision Planning in Mixed-precision Over-the-Air Federated Learning

arXiv 2503.15569 ↗First posted 19 Mar 2025cs.LG cs.HCcited by 1

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Abstract (arXiv)

Mixed-precision computing, a widely applied technique in AI, offers a larger trade-off space between accuracy and efficiency. The recent purposed Mixed-Precision Over-the-Air Federated Learning (MP-OTA-FL) enables clients to operate at appropriate precision levels based on their heterogeneous hardware, taking advantages of the larger trade-off space while covering the quantization overheads in the mixed-precision modulation scheme for the OTA aggregation process. A key to further exploring the potential of the MP-OTA-FL framework is the optimization of client precision levels. The choice of precision level hinges on multifaceted factors including hardware capability, potential client contribution, and user satisfaction, among which factors can be difficult to define or quantify. In this paper, we propose a RAG-based User Profiling for precision planning framework that integrates retrieval-augmented LLMs and dynamic client profiling to optimize satisfaction and contributions. This includes a hybrid interface for gathering device/user insights and an RAG database storing historical quantization decisions with feedback. Experiments show that our method boosts satisfaction, energy savings, and global model accuracy in MP-OTA-FL systems.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 13 references that are in the map. First posted on arXiv 19 Mar 2025.

Builds on

9 of 13 references.

References · 9

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2019Common Voice: A Massively-Multilingual Speech Corpuscited by 1,384
  4. 2017Mixed Precision Trainingcited by 1,156
  5. 2015Deep Speech 2: End-to-End Speech Recognition in English and Mandarincited by 451
  6. 2019Towards Efficient Data Valuation Based on the Shapley Valuecited by 281
  7. 2018Federated Learning via Over-the-Air Computationcited by 219
  8. 2024A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)cited by 58
  9. 2024Mixed-Precision Federated Learning via Multi-Precision Over-The-Air Aggregationcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

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  1. 2024FedShift: Robust Federated Learning Aggregation Scheme in Resource Constrained Environment via Weight Shifting
  2. 2025Non-Convex Over-the-Air Heterogeneous Federated Learning: A Bias-Variance Trade-off
  3. 2023Mixed-Precision Quantization for Federated Learning on Resource-Constrained Heterogeneous Devices
  4. 2024Personalized Over-the-Air Federated Learning with Personalized Reconfigurable Intelligent Surfaces
  5. 2025Over-the-Air Federated Learning: Rethinking Edge AI Through Signal Processing
  6. 2024FedTSA: A Cluster-based Two-Stage Aggregation Method for Model-heterogeneous Federated Learning
  7. 2025FLEX-MoE: Federated Mixture-of-Experts with Load-balanced Expert Assignment for Edge Computing
  8. 2026Quantization in Federated Learning: Methods, Challenges and Future Directions