Generative AI like ChatGPT in Blockchain Federated Learning: use cases, opportunities and future
- URL: http://arxiv.org/abs/2407.18358v1
- Date: Thu, 25 Jul 2024 19:43:49 GMT
- Title: Generative AI like ChatGPT in Blockchain Federated Learning: use cases, opportunities and future
- Authors: Sai Puppala, Ismail Hossain, Md Jahangir Alam, Sajedul Talukder, Jannatul Ferdaus, Mahedi Hasan, Sameera Pisupati, Shanmukh Mathukumilli,
- Abstract summary: 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.
- Score: 4.497001527881303
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Federated learning has become a significant approach for training machine learning models using decentralized data without necessitating the sharing of this data. Recently, the incorporation of generative artificial intelligence (AI) methods has provided new possibilities for improving privacy, augmenting data, and customizing models. This research explores potential integrations of generative AI in federated learning, revealing various opportunities to enhance privacy, data efficiency, and model performance. It particularly emphasizes the importance of generative models like generative adversarial networks (GANs) and variational autoencoders (VAEs) in creating synthetic data that replicates the distribution of real data. Generating synthetic data helps federated learning address challenges related to limited data availability and supports robust model development. Additionally, we examine various applications of generative AI in federated learning that enable more personalized solutions.
Related papers
- Exploring the Landscape for Generative Sequence Models for Specialized Data Synthesis [0.0]
This paper introduces a novel approach that leverages three generative models of varying complexity to synthesize Malicious Network Traffic.
Our approach transforms numerical data into text, re-framing data generation as a language modeling task.
Our method surpasses state-of-the-art generative models in producing high-fidelity synthetic data.
arXiv Detail & Related papers (2024-11-04T09:51:10Z) - On the Challenges and Opportunities in Generative AI [135.2754367149689]
We argue that current large-scale generative AI models do not sufficiently address several fundamental issues that hinder their widespread adoption across domains.
In this work, we aim to identify key unresolved challenges in modern generative AI paradigms that should be tackled to further enhance their capabilities, versatility, and reliability.
arXiv Detail & Related papers (2024-02-28T15:19:33Z) - Personalized Federated Learning with Contextual Modulation and
Meta-Learning [2.7716102039510564]
Federated learning has emerged as a promising approach for training machine learning models on decentralized data sources.
We propose a novel framework that combines federated learning with meta-learning techniques to enhance both efficiency and generalization capabilities.
arXiv Detail & Related papers (2023-12-23T08:18:22Z) - AI-Generated Images as Data Source: The Dawn of Synthetic Era [61.879821573066216]
generative AI has unlocked the potential to create synthetic images that closely resemble real-world photographs.
This paper explores the innovative concept of harnessing these AI-generated images as new data sources.
In contrast to real data, AI-generated data exhibit remarkable advantages, including unmatched abundance and scalability.
arXiv Detail & Related papers (2023-10-03T06:55:19Z) - Federated Learning-Empowered AI-Generated Content in Wireless Networks [58.48381827268331]
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.
arXiv Detail & Related papers (2023-07-14T04:13:11Z) - Phoenix: A Federated Generative Diffusion Model [6.09170287691728]
Training generative models on large centralized datasets can pose challenges in terms of data privacy, security, and accessibility.
This paper proposes a novel method for training a Denoising Diffusion Probabilistic Model (DDPM) across multiple data sources using Federated Learning (FL) techniques.
arXiv Detail & Related papers (2023-06-07T01:43:09Z) - Privacy-Preserving Machine Learning for Collaborative Data Sharing via
Auto-encoder Latent Space Embeddings [57.45332961252628]
Privacy-preserving machine learning in data-sharing processes is an ever-critical task.
This paper presents an innovative framework that uses Representation Learning via autoencoders to generate privacy-preserving embedded data.
arXiv Detail & Related papers (2022-11-10T17:36:58Z) - FedSyn: Synthetic Data Generation using Federated Learning [0.0]
Current Machine Learning practices can be leveraged to generate synthetic data from an existing dataset.
Data privacy concerns that some institutions may not be comfortable with.
This paper proposes a novel approach to generate synthetic data - FedSyn.
arXiv Detail & Related papers (2022-03-11T14:05:37Z) - Privacy-preserving Generative Framework Against Membership Inference
Attacks [10.791983671720882]
We design a privacy-preserving generative framework against membership inference attacks.
We first map the source data to the latent space through the VAE model to get the latent code, then perform noise process satisfying metric privacy on the latent code, and finally use the VAE model to reconstruct the synthetic data.
Our experimental evaluation demonstrates that the machine learning model trained with newly generated synthetic data can effectively resist membership inference attacks and still maintain high utility.
arXiv Detail & Related papers (2022-02-11T06:13:30Z) - Non-IID data and Continual Learning processes in Federated Learning: A
long road ahead [58.720142291102135]
Federated Learning is a novel framework that allows multiple devices or institutions to train a machine learning model collaboratively while preserving their data private.
In this work, we formally classify data statistical heterogeneity and review the most remarkable learning strategies that are able to face it.
At the same time, we introduce approaches from other machine learning frameworks, such as Continual Learning, that also deal with data heterogeneity and could be easily adapted to the Federated Learning settings.
arXiv Detail & Related papers (2021-11-26T09:57:11Z) - Rethinking Architecture Design for Tackling Data Heterogeneity in
Federated Learning [53.73083199055093]
We show that attention-based architectures (e.g., Transformers) are fairly robust to distribution shifts.
Our experiments show that replacing convolutional networks with Transformers can greatly reduce catastrophic forgetting of previous devices.
arXiv Detail & Related papers (2021-06-10T21:04:18Z)
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.