Unlocking the Potential of Collaborative AI -- On the Socio-technical Challenges of Federated Machine Learning

• URL: http://arxiv.org/abs/2304.13688v3
• Date: Sat, 29 Apr 2023 00:15:47 GMT
• Title: Unlocking the Potential of Collaborative AI -- On the Socio-technical Challenges of Federated Machine Learning
• Authors: Tobias M\"uller, Milena Zahn and Florian Matthes
• Abstract summary: Federated Machine Learning is a novel AI paradigm enabling the creation of AI models from decentralized, potentially siloed data. This research investigates the challenges of prevailing collaborative business models and distinct aspects of Federated Machine Learning.
• Score: 0.6767885381740952
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The disruptive potential of AI systems roots in the emergence of big data. Yet, a significant portion is scattered and locked in data silos, leaving its potential untapped. Federated Machine Learning is a novel AI paradigm enabling the creation of AI models from decentralized, potentially siloed data. Hence, Federated Machine Learning could technically open data silos and therefore unlock economic potential. However, this requires collaboration between multiple parties owning data silos. Setting up collaborative business models is complex and often a reason for failure. Current literature lacks guidelines on which aspects must be considered to successfully realize collaborative AI projects. This research investigates the challenges of prevailing collaborative business models and distinct aspects of Federated Machine Learning. Through a systematic literature review, focus group, and expert interviews, we provide a systemized collection of socio-technical challenges and an extended Business Model Canvas for the initial viability assessment of collaborative AI projects.

Related papers

• Data Analysis in the Era of Generative AI [56.44807642944589]
  This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges. We explore how the emergence of large language and multimodal models offers new opportunities to enhance various stages of data analysis workflow. We then examine human-centered design principles that facilitate intuitive interactions, build user trust, and streamline the AI-assisted analysis workflow across multiple apps.
  arXiv  Detail & Related papers  (2024-09-27T06:31:03Z)
• 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)
• Can AI Serve as a Substitute for Human Subjects in Software Engineering Research? [24.39463126056733]
  This vision paper proposes a novel approach to qualitative data collection in software engineering research by harnessing the capabilities of artificial intelligence (AI) We explore the potential of AI-generated synthetic text as an alternative source of qualitative data. We discuss the prospective development of new foundation models aimed at emulating human behavior in observational studies and user evaluations.
  arXiv  Detail & Related papers  (2023-11-18T14:05:52Z)
• The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence [67.70415658080121]
  Recent advances in machine learning and AI are disrupting technological innovation, product development, and society as a whole. AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access. Here we explore and investigate aspects of an AI-driven, automated, closed-loop approach to scientific discovery.
  arXiv  Detail & Related papers  (2023-07-09T21:16:56Z)
• AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges [60.56413461109281]
  Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big data generated by IT Operations processes. We discuss in depth the key types of data emitted by IT Operations activities, the scale and challenges in analyzing them, and where they can be helpful. We categorize the key AIOps tasks as - incident detection, failure prediction, root cause analysis and automated actions.
  arXiv  Detail & Related papers  (2023-04-10T15:38:12Z)
• Human-Centric Multimodal Machine Learning: Recent Advances and Testbed on AI-based Recruitment [66.91538273487379]
  There is a certain consensus about the need to develop AI applications with a Human-Centric approach. Human-Centric Machine Learning needs to be developed based on four main requirements: (i) utility and social good; (ii) privacy and data ownership; (iii) transparency and accountability; and (iv) fairness in AI-driven decision-making processes. We study how current multimodal algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data.
  arXiv  Detail & Related papers  (2023-02-13T16:44:44Z)
• APPFLChain: A Privacy Protection Distributed Artificial-Intelligence Architecture Based on Federated Learning and Consortium Blockchain [6.054775780656853]
  We propose a new system architecture called APPFLChain. It is an integrated architecture of a Hyperledger Fabric-based blockchain and a federated-learning paradigm. Our new system can maintain a high degree of security and privacy as users do not need to share sensitive personal information to the server.
  arXiv  Detail & Related papers  (2022-06-26T05:30:07Z)
• INTERN: A New Learning Paradigm Towards General Vision [117.3343347061931]
  We develop a new learning paradigm named INTERN. By learning with supervisory signals from multiple sources in multiple stages, the model being trained will develop strong generalizability. In most cases, our models, adapted with only 10% of the training data in the target domain, outperform the counterparts trained with the full set of data.
  arXiv  Detail & Related papers  (2021-11-16T18:42:50Z)
• Decentralized Federated Learning Preserves Model and Data Privacy [77.454688257702]
  We propose a fully decentralized approach, which allows to share knowledge between trained models. Students are trained on the output of their teachers via synthetically generated input data. The results show that an untrained student model, trained on the teachers output reaches comparable F1-scores as the teacher.
  arXiv  Detail & Related papers  (2021-02-01T14:38:54Z)
• Enterprise AI Canvas -- Integrating Artificial Intelligence into Business [0.0]
  The Enterprise AI canvas is designed to bring Data Scientist and business expert together to discuss and define all relevant aspects. It consists of two parts where part one focuses on the business view and organizational aspects, whereas part two focuses on the underlying machine learning model and the data it uses.
  arXiv  Detail & Related papers  (2020-09-18T07:30:56Z)

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.