CollaFuse: Navigating Limited Resources and Privacy in Collaborative Generative AI

• URL: http://arxiv.org/abs/2402.19105v2
• Date: Fri, 16 Aug 2024 11:28:13 GMT
• Title: CollaFuse: Navigating Limited Resources and Privacy in Collaborative Generative AI
• Authors: Domenique Zipperling, Simeon Allmendinger, Lukas Struppek, Niklas Kühl,
• Abstract summary: CollaFuse is a novel framework inspired by split learning. It enables shared server training and inference, alleviating client computational burdens. It has the potential to impact various application areas, such as the design of edge computing solutions, healthcare research, or autonomous driving.
• Score: 5.331052581441263
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In the landscape of generative artificial intelligence, diffusion-based models present challenges for socio-technical systems in data requirements and privacy. Traditional approaches like federated learning distribute the learning process but strain individual clients, especially with constrained resources (e.g., edge devices). In response to these challenges, we introduce CollaFuse, a novel framework inspired by split learning. Tailored for efficient and collaborative use of denoising diffusion probabilistic models, CollaFuse enables shared server training and inference, alleviating client computational burdens. This is achieved by retaining data and computationally inexpensive GPU processes locally at each client while outsourcing the computationally expensive processes to the shared server. Demonstrated in a healthcare context, CollaFuse enhances privacy by highly reducing the need for sensitive information sharing. These capabilities hold the potential to impact various application areas, such as the design of edge computing solutions, healthcare research, or autonomous driving. In essence, our work advances distributed machine learning, shaping the future of collaborative GenAI networks.

Related papers

• 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)
• CollaFuse: Collaborative Diffusion Models [5.331052581441263]
  We introduce a novel approach for distributed collaborative diffusion models inspired by split learning. Our approach facilitates collaborative training of diffusion models while alleviating client computational burdens during image synthesis.
  arXiv  Detail & Related papers  (2024-06-20T15:54:21Z)
• 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)
• 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)
• Scalable Collaborative Learning via Representation Sharing [53.047460465980144]
  Federated learning (FL) and Split Learning (SL) are two frameworks that enable collaborative learning while keeping the data private (on device) In FL, each data holder trains a model locally and releases it to a central server for aggregation. In SL, the clients must release individual cut-layer activations (smashed data) to the server and wait for its response (during both inference and back propagation). In this work, we present a novel approach for privacy-preserving machine learning, where the clients collaborate via online knowledge distillation using a contrastive loss.
  arXiv  Detail & Related papers  (2022-11-20T10:49:22Z)
• 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)
• Federated Pruning: Improving Neural Network Efficiency with Federated Learning [24.36174705715827]
  We propose Federated Pruning to train a reduced model under the federated setting. We explore different pruning schemes and provide empirical evidence of the effectiveness of our methods.
  arXiv  Detail & Related papers  (2022-09-14T00:48:37Z)
• Privacy-Preserving Serverless Edge Learning with Decentralized Small Data [13.254530176359182]
  Distributed training strategies have recently become a promising approach to ensure data privacy when training deep models. This paper extends conventional serverless platforms with serverless edge learning architectures and provides an efficient distributed training framework from the networking perspective.
  arXiv  Detail & Related papers  (2021-11-29T21:04:49Z)
• A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and Comparison [0.6299766708197883]
  Pervasive computing promotes the installation of connected devices in our living spaces in order to provide services. Two major developments have gained significant momentum recently: an advanced use of edge resources and the integration of machine learning techniques for engineering applications. We propose a novel aggregation algorithm, termed FedDist, which is able to modify its model architecture by identifying dissimilarities between specific neurons amongst the clients.
  arXiv  Detail & Related papers  (2021-10-19T19:43:28Z)
• Distributed Deep Learning in Open Collaborations [49.240611132653456]
  We propose a novel algorithmic framework designed specifically for collaborative training. We demonstrate the effectiveness of our approach for SwAV and ALBERT pretraining in realistic conditions and achieve performance comparable to traditional setups at a fraction of the cost.
  arXiv  Detail & Related papers  (2021-06-18T16:23:13Z)
• A Privacy-Preserving Distributed Architecture for Deep-Learning-as-a-Service [68.84245063902908]
  This paper introduces a novel distributed architecture for deep-learning-as-a-service. It is able to preserve the user sensitive data while providing Cloud-based machine and deep learning services.
  arXiv  Detail & Related papers  (2020-03-30T15:12:03Z)

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.