CommunityAI: Towards Community-based Federated Learning
- URL: http://arxiv.org/abs/2311.17958v1
- Date: Wed, 29 Nov 2023 09:31:52 GMT
- Title: CommunityAI: Towards Community-based Federated Learning
- Authors: Ilir Murturi, Praveen Kumar Donta, Schahram Dustdar
- Abstract summary: We present a novel framework for Community-based Federated Learning called CommunityAI.
CommunityAI enables participants to be organized into communities based on their shared interests, expertise, or data characteristics.
We discuss the conceptual architecture, system requirements, processes, and future challenges that must be solved.
- Score: 6.535815174238974
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Federated Learning (FL) has emerged as a promising paradigm to train machine
learning models collaboratively while preserving data privacy. However, its
widespread adoption faces several challenges, including scalability,
heterogeneous data and devices, resource constraints, and security concerns.
Despite its promise, FL has not been specifically adapted for community
domains, primarily due to the wide-ranging differences in data types and
context, devices and operational conditions, environmental factors, and
stakeholders. In response to these challenges, we present a novel framework for
Community-based Federated Learning called CommunityAI. CommunityAI enables
participants to be organized into communities based on their shared interests,
expertise, or data characteristics. Community participants collectively
contribute to training and refining learning models while maintaining data and
participant privacy within their respective groups. Within this paper, we
discuss the conceptual architecture, system requirements, processes, and future
challenges that must be solved. Finally, our goal within this paper is to
present our vision regarding enabling a collaborative learning process within
various communities.
Related papers
- Advances in Robust Federated Learning: Heterogeneity Considerations [25.261572089655264]
Key challenge is to efficiently train models across multiple clients with different data distributions, model structures, task objectives, computational capabilities, and communication resources.
In this paper, we first outline the basic concepts of heterogeneous federated learning.
We then summarize the research challenges in federated learning in terms of five aspects: data, model, task, device, and communication.
arXiv Detail & Related papers (2024-05-16T06:35:42Z) - Private Knowledge Sharing in Distributed Learning: A Survey [50.51431815732716]
The rise of Artificial Intelligence has revolutionized numerous industries and transformed the way society operates.
It is crucial to utilize information in learning processes that are either distributed or owned by different entities.
Modern data-driven services have been developed to integrate distributed knowledge entities into their outcomes.
arXiv Detail & Related papers (2024-02-08T07:18:23Z) - Unveiling Diversity: Empowering OSS Project Leaders with Community
Diversity and Turnover Dashboards [51.67585198094836]
CommunityTapestry is a dynamic real-time community dashboard.
It presents key diversity and turnover signals that we identified from the literature.
It helped project leaders identify areas of improvement and gave them actionable information.
arXiv Detail & Related papers (2023-12-13T22:12:57Z) - Security and Privacy Issues of Federated Learning [0.0]
Federated Learning (FL) has emerged as a promising approach to address data privacy and confidentiality concerns.
This paper presents a comprehensive taxonomy of security and privacy challenges in Federated Learning (FL) across various machine learning models.
arXiv Detail & Related papers (2023-07-22T22:51:07Z) - 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) - Federated Learning: Balancing the Thin Line Between Data Intelligence
and Privacy [0.0]
Federated learning holds great promise in learning from fragmented sensitive data.
This article provides a systematic overview and detailed taxonomy of federated learning.
We investigate the existing security challenges in federated learning and provide an overview of established defense techniques for data poisoning, inference attacks, and model poisoning attacks.
arXiv Detail & Related papers (2022-04-22T23:39:16Z) - Efficient Split-Mix Federated Learning for On-Demand and In-Situ
Customization [107.72786199113183]
Federated learning (FL) provides a distributed learning framework for multiple participants to collaborate learning without sharing raw data.
In this paper, we propose a novel Split-Mix FL strategy for heterogeneous participants that, once training is done, provides in-situ customization of model sizes and robustness.
arXiv Detail & Related papers (2022-03-18T04:58:34Z) - Fairness in Federated Learning for Spatial-Temporal Applications [9.333236221677046]
Federated learning involves training statistical models over remote devices such as mobile phones.
We discuss the current metrics and approaches that are available to measure and evaluate fairness in the context of spatial-temporal models.
We propose how these metrics and approaches can be re-defined to address the challenges that are faced in the federated learning setting.
arXiv Detail & Related papers (2022-01-17T19:23:15Z) - 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) - Towards Explainable Multi-Party Learning: A Contrastive Knowledge
Sharing Framework [23.475874929905192]
We propose a novel contrastive multi-party learning framework for knowledge refinement and sharing.
The proposed scheme achieves significant improvement in model performance in a variety of scenarios.
arXiv Detail & Related papers (2021-04-14T07:33:48Z) - Federated Learning: A Signal Processing Perspective [144.63726413692876]
Federated learning is an emerging machine learning paradigm for training models across multiple edge devices holding local datasets, without explicitly exchanging the data.
This article provides a unified systematic framework for federated learning in a manner that encapsulates and highlights the main challenges that are natural to treat using signal processing tools.
arXiv Detail & Related papers (2021-03-31T15:14:39Z)
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.