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

Zihan Chen, Howard H. Yang, Y. C. Tay, Kai Fong Ernest Chong, Tony Q. S. Quek

TL;DR

This article surveys how foundation models (FMs) and federated learning (FL) can be integrated into wireless networks to enable distributed, intelligent edge systems. It analyzes the significant resource, latency, and privacy challenges posed by large FMs in FL settings and proposes architectures and paradigms such as FM-as-a-Service (FMaaS), cloud-edge collaboration, and data augmentation for FL preprocessing. It further explores how FMs can enhance FL training and evaluation through teacher-student distillation, data synthesis, and model benchmarking, while presenting practical approaches like hybrid global-local training and parameter-efficient fine-tuning (PEFT) to manage costs. The work highlights future directions including incentive mechanisms, joint QoS-resource optimization, privacy and robustness concerns, task scheduling, and protocol design for FM transmission, aiming to enable robust and sustainable FM-enabled federated wireless networks.

Abstract

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.

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

TL;DR

This article surveys how foundation models (FMs) and federated learning (FL) can be integrated into wireless networks to enable distributed, intelligent edge systems. It analyzes the significant resource, latency, and privacy challenges posed by large FMs in FL settings and proposes architectures and paradigms such as FM-as-a-Service (FMaaS), cloud-edge collaboration, and data augmentation for FL preprocessing. It further explores how FMs can enhance FL training and evaluation through teacher-student distillation, data synthesis, and model benchmarking, while presenting practical approaches like hybrid global-local training and parameter-efficient fine-tuning (PEFT) to manage costs. The work highlights future directions including incentive mechanisms, joint QoS-resource optimization, privacy and robustness concerns, task scheduling, and protocol design for FM transmission, aiming to enable robust and sustainable FM-enabled federated wireless networks.

Abstract

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.
Paper Structure (15 sections, 4 figures, 2 tables)

This paper contains 15 sections, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Paradigms, challenges, and opportunities for the integrated FM and FL
  3. Preliminaries of FMs and FL
  4. Challenges of deploying FMs over FL-enabled wireless networks
  5. Possible network architectures for integrating FM and FL
  6. FMs in FL-enabled wireless networks
  7. Foundation models in pre-processing stages
  8. Foundation model in the training
  9. Foundation models for model evaluation:
  10. Potential issues
  11. FL for training FMs over networks
  12. Hybrid training for FM over networks
  13. Parameter-efficient fine-tuning
  14. Future Trends and Open Issues
  15. Concluding Remarks

Figures (4)

  • Figure 1: An example of an intelligent transportation system over wireless networks, where the autonomous vehicles and edge server collaboratively perform low-latency decision-making assisted by FMs, such as route planning, traffic re-routing, and crowd management.
  • Figure 2: A brief overview of vanilla machine learning paradigm in FL-enabled wireless networks.
  • Figure 3: An overview of different types of architecture of integrated FMs in the federated edge learning system. FMs are deployed at the cloud server (1), edge server (2), and local clients (3), respectively.
  • Figure 4: An overview of different types of training paradigms of FMs in the federated edge learning system with cloud FM pre-training. In PEFT-based paradigms, the parameters of pre-trained FMs, depicted as gray blocks, will remain intact, and only a small portion of parameters (e.g., adapter weights and prompt vectors) will be updated and transmitted.