# Federated Learning: Organizational Opportunities, Challenges, and Adoption Strategies

arXiv: https://arxiv.org/abs/2308.02219 · HTML: https://mlsanity.com/paper/2308.02219/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 84 references that are in the map.
- First posted on arXiv 4 Aug 2023.

## Builds on (most cited first, 15 of 15)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Federated Learning with Non-IID Data](https://mlsanity.com/paper/1806.00582/) (2018)
- [How To Backdoor Federated Learning](https://mlsanity.com/paper/1807.00459/) (2018)
- [The Future of Digital Health with Federated Learning](https://mlsanity.com/paper/2003.08119/) (2020)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models](https://mlsanity.com/paper/2007.03051/) (2020)
- [Asynchronous Federated Learning on Heterogeneous Devices: A Survey](https://mlsanity.com/paper/2109.04269/) (2021)
- [Demystifying the Draft EU Artificial Intelligence Act](https://mlsanity.com/paper/2107.03721/) (2021)
- [Federated Learning Enables Big Data for Rare Cancer Boundary Detection](https://mlsanity.com/paper/2204.10836/) (2022)
- [Federated Mutual Learning](https://mlsanity.com/paper/2006.16765/) (2020)
- [Decentralized Federated Learning through Proxy Model Sharing](https://mlsanity.com/paper/2111.11343/) (2021)
- [Man-in-the-Middle Attacks against Machine Learning Classifiers via Malicious Generative Models](https://mlsanity.com/paper/1910.06838/) (2019)
- [Fairness, Integrity, and Privacy in a Scalable Blockchain-based Federated Learning System](https://mlsanity.com/paper/2111.06290/) (2021)
- [Zero-Knowledge Proof-based Practical Federated Learning on Blockchain](https://mlsanity.com/paper/2304.05590/) (2023)

## Abstract (arXiv)

Restrictive rules for data sharing in many industries have led to the development of federated learning. Federated learning is a machine-learning technique that allows distributed clients to train models collaboratively without the need to share their respective training data with others. In this paper, we first explore the technical foundations of federated learning and its organizational opportunities. Second, we present a conceptual framework for the adoption of federated learning, mapping four types of organizations by their artificial intelligence capabilities and limits to data sharing. We then discuss why exemplary organizations in different contexts - including public authorities, financial service providers, manufacturing companies, as well as research and development consortia - might consider different approaches to federated learning. To conclude, we argue that federated learning presents organizational challenges with ample interdisciplinary opportunities for information systems researchers.