Federated Learning-Empowered AI-Generated Content in Wireless Networks
- URL: http://arxiv.org/abs/2307.07146v1
- Date: Fri, 14 Jul 2023 04:13:11 GMT
- Title: Federated Learning-Empowered AI-Generated Content in Wireless Networks
- Authors: Xumin Huang, Peichun Li, Hongyang Du, Jiawen Kang, Dusit Niyato, Dong
In Kim, Yuan Wu
- Abstract summary: Federated learning (FL) can be leveraged to improve learning efficiency and achieve privacy protection for AIGC.
We present FL-based techniques for empowering AIGC, and aim to enable users to generate diverse, personalized, and high-quality content.
- Score: 58.48381827268331
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Artificial intelligence generated content (AIGC) has emerged as a promising
technology to improve the efficiency, quality, diversity and flexibility of the
content creation process by adopting a variety of generative AI models.
Deploying AIGC services in wireless networks has been expected to enhance the
user experience. However, the existing AIGC service provision suffers from
several limitations, e.g., the centralized training in the pre-training,
fine-tuning and inference processes, especially their implementations in
wireless networks with privacy preservation. Federated learning (FL), as a
collaborative learning framework where the model training is distributed to
cooperative data owners without the need for data sharing, can be leveraged to
simultaneously improve learning efficiency and achieve privacy protection for
AIGC. To this end, we present FL-based techniques for empowering AIGC, and aim
to enable users to generate diverse, personalized, and high-quality content.
Furthermore, we conduct a case study of FL-aided AIGC fine-tuning by using the
state-of-the-art AIGC model, i.e., stable diffusion model. Numerical results
show that our scheme achieves advantages in effectively reducing the
communication cost and training latency and privacy protection. Finally, we
highlight several major research directions and open issues for the convergence
of FL and AIGC.
Related papers
- Intelligent Mobile AI-Generated Content Services via Interactive Prompt Engineering and Dynamic Service Provisioning [55.641299901038316]
AI-generated content can organize collaborative Mobile AIGC Service Providers (MASPs) at network edges to provide ubiquitous and customized content for resource-constrained users.
Such a paradigm faces two significant challenges: 1) raw prompts often lead to poor generation quality due to users' lack of experience with specific AIGC models, and 2) static service provisioning fails to efficiently utilize computational and communication resources.
We develop an interactive prompt engineering mechanism that leverages a Large Language Model (LLM) to generate customized prompt corpora and employs Inverse Reinforcement Learning (IRL) for policy imitation.
arXiv Detail & Related papers (2025-02-17T03:05:20Z) - Advancing Personalized Federated Learning: Integrative Approaches with AI for Enhanced Privacy and Customization [0.0]
This paper proposes a novel approach that enhances PFL with cutting-edge AI techniques.
We present a model that boosts the performance of individual client models and ensures robust privacy-preserving mechanisms.
This work paves the way for a new era of truly personalized and privacy-conscious AI systems.
arXiv Detail & Related papers (2025-01-30T07:03:29Z) - Generative AI like ChatGPT in Blockchain Federated Learning: use cases, opportunities and future [4.497001527881303]
This research explores potential integrations of generative AI in federated learning.
generative adversarial networks (GANs) and variational autoencoders (VAEs)
Generating synthetic data helps federated learning address challenges related to limited data availability.
arXiv Detail & Related papers (2024-07-25T19:43:49Z) - When Swarm Learning meets energy series data: A decentralized collaborative learning design based on blockchain [10.099134773737939]
Machine learning models offer the capability to forecast future energy production or consumption.
However, legal and policy constraints within specific energy sectors present technical hurdles in utilizing data from diverse sources.
We propose adopting a Swarm Learning scheme, which replaces the centralized server with a blockchain-based distributed network.
arXiv Detail & Related papers (2024-06-07T08:42:26Z) - A Learning-based Incentive Mechanism for Mobile AIGC Service in Decentralized Internet of Vehicles [49.86094523878003]
We propose a decentralized incentive mechanism for mobile AIGC service allocation.
We employ multi-agent deep reinforcement learning to find the balance between the supply of AIGC services on RSUs and user demand for services within the IoV context.
arXiv Detail & Related papers (2024-03-29T12:46:07Z) - Large Language Models Empowered Autonomous Edge AI for Connected
Intelligence [51.269276328087855]
Edge artificial intelligence (Edge AI) is a promising solution to achieve connected intelligence.
This article presents a vision of autonomous edge AI systems that automatically organize, adapt, and optimize themselves to meet users' diverse requirements.
arXiv Detail & Related papers (2023-07-06T05:16:55Z) - Optimization Design for Federated Learning in Heterogeneous 6G Networks [27.273745760946962]
Federated learning (FL) is anticipated to be a key enabler for achieving ubiquitous AI in 6G networks.
There are several system and statistical heterogeneity challenges for effective and efficient FL implementation in 6G networks.
In this article, we investigate the optimization approaches that can effectively address the challenges.
arXiv Detail & Related papers (2023-03-15T02:18:21Z) - Personalizing Federated Learning with Over-the-Air Computations [84.8089761800994]
Federated edge learning is a promising technology to deploy intelligence at the edge of wireless networks in a privacy-preserving manner.
Under such a setting, multiple clients collaboratively train a global generic model under the coordination of an edge server.
This paper presents a distributed training paradigm that employs analog over-the-air computation to address the communication bottleneck.
arXiv Detail & Related papers (2023-02-24T08:41:19Z) - Enabling AI-Generated Content (AIGC) Services in Wireless Edge Networks [68.00382171900975]
In wireless edge networks, the transmission of incorrectly generated content may unnecessarily consume network resources.
We present the AIGC-as-a-service concept and discuss the challenges in deploying A at the edge networks.
We propose a deep reinforcement learning-enabled algorithm for optimal ASP selection.
arXiv Detail & Related papers (2023-01-09T09:30:23Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.