mlsanityData to 7 Oct 2026

The Role of Federated Learning in a Wireless World with Foundation Models

arXiv 2310.04003 ↗First posted 6 Oct 2023cs.NI cs.DC cs.LGcited by 7

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Abstract (arXiv)

Foundation models (FMs) are general-purpose artificial intelligence (AI) models that have recently enabled multiple brand-new generative AI applications. The rapid advances in FMs serve as an important contextual backdrop for the vision of next-generation wireless networks, where federated learning (FL) is a key enabler of distributed network intelligence. Currently, the exploration of the interplay between FMs and FL is still in its nascent stage. Naturally, FMs are capable of boosting the performance of FL, and FL could also leverage decentralized data and computing resources to assist in the training of FMs. However, the exceptionally high requirements that FMs have for computing resources, storage, and communication overhead would pose critical challenges to FL-enabled wireless networks. In this article, we explore the extent to which FMs are suitable for FL over wireless networks, including a broad overview of research challenges and opportunities. In particular, we discuss multiple new paradigms for realizing future intelligent networks that integrate FMs and FL. We also consolidate several broad research directions associated with these paradigms.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 15 of its 22 references that are in the map. First posted on arXiv 6 Oct 2023.

Builds on

15 of 22 references.

References · 15

  1. 2022Training language models to follow instructions with human feedbackcited by 15,712
  2. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  3. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  4. 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
  5. 2022Emergent Abilities of Large Language Modelscited by 2,483
  6. 2023AWQ: Activation-aware Weight Quantization for LLM Compression and Accelerationcited by 1,234
  7. 2021A Field Guide to Federated Optimizationcited by 312
  8. 2021Generating Datasets with Pretrained Language Modelscited by 192
  9. 2022Distilling Reasoning Capabilities into Smaller Language Modelscited by 155
  10. 2022PromptFL: Let Federated Participants Cooperatively Learn Prompts Instead of Models -- Federated Learning in Age of Foundation Modelcited by 133
  11. 2018Wireless Network Intelligence at the Edgecited by 90
  12. 2023DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuningcited by 34
3 more
  1. 2023Low-Parameter Federated Learning with Large Language Modelscited by 22
  2. 2023Enabling AI-Generated Content (AIGC) Services in Wireless Edge Networkscited by 21
  3. 2022Twelve Scientific Challenges for 6G: Rethinking the Foundations of Communications Theorycited by 15

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 3; 2026: 2.

7 papers.

  1. 2024An overview of domain-specific foundation model: key technologies, applications and challengescited by 19
  2. 2025Federated Intelligence: When Large AI Models Meet Federated Fine-Tuning and Collaborative Reasoning at the Network Edgecited by 4
  3. 2026Multi-Modal Data-Enhanced Foundation Models for Prediction and Control in Wireless Networks: A Surveycited by 3
  4. 2024Federated Low-Rank Adaptation with Differential Privacy over Wireless Networkscited by 1
  5. 2025Edge-Assisted Collaborative Fine-Tuning for Multi-User Personalized Artificial Intelligence Generated Content (AIGC)cited by 1
  6. 2025A Federated Fine-Tuning Paradigm of Foundation Models in Heterogenous Wireless Networkscited by 0
  7. 2026Priority-Aware Learning-Unlearning Correction for Dynamic Decentralized LoRA Fine-Tuningcited by 0