# Federated learning: Applications, challenges and future directions

arXiv: https://arxiv.org/abs/2205.09513 · HTML: https://mlsanity.com/paper/2205.09513/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 57 of its 155 references that are in the map.
- First posted on arXiv 18 May 2022.

## Builds on (most cited first, 30 of 57)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Federated Learning with Non-IID Data](https://mlsanity.com/paper/1806.00582/) (2018)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [How To Backdoor Federated Learning](https://mlsanity.com/paper/1807.00459/) (2018)
- [Federated Multi-Task Learning](https://mlsanity.com/paper/1705.10467/) (2017)
- [The Future of Digital Health with Federated Learning](https://mlsanity.com/paper/2003.08119/) (2020)
- [Personalized Federated Learning with Moreau Envelopes](https://mlsanity.com/paper/2006.08848/) (2020)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training](https://mlsanity.com/paper/1712.01887/) (2017)
- [Differentially Private Federated Learning: A Client Level Perspective](https://mlsanity.com/paper/1712.07557/) (2017)
- [Agnostic Federated Learning](https://mlsanity.com/paper/1902.00146/) (2019)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [Split learning for health: Distributed deep learning without sharing raw patient data](https://mlsanity.com/paper/1812.00564/) (2018)
- [Fair Resource Allocation in Federated Learning](https://mlsanity.com/paper/1905.10497/) (2019)
- [Fairness Without Demographics in Repeated Loss Minimization](https://mlsanity.com/paper/1806.08010/) (2018)
- [Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning](https://mlsanity.com/paper/1702.07464/) (2017)
- [Three Approaches for Personalization with Applications to Federated Learning](https://mlsanity.com/paper/2002.10619/) (2020)
- [Distributed learning of deep neural network over multiple agents](https://mlsanity.com/paper/1810.06060/) (2018)
- [Federated Learning over Wireless Fading Channels](https://mlsanity.com/paper/1907.09769/) (2019)
- [FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare](https://mlsanity.com/paper/1907.09173/) (2019)
- [Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption](https://mlsanity.com/paper/1711.10677/) (2017)
- [Adaptive Neural Networks for Efficient Inference](https://mlsanity.com/paper/1702.07811/) (2017)
- [cpSGD: Communication-efficient and differentially-private distributed SGD](https://mlsanity.com/paper/1805.10559/) (2018)
- [A Survey on Homomorphic Encryption Schemes: Theory and Implementation](https://mlsanity.com/paper/1704.03578/) (2017)
- [Federated Learning for Wireless Communications: Motivation, Opportunities and Challenges](https://mlsanity.com/paper/1908.06847/) (2019)
- [Hierarchical Federated Learning Across Heterogeneous Cellular Networks](https://mlsanity.com/paper/1909.02362/) (2019)
- [Reliable Federated Learning for Mobile Networks](https://mlsanity.com/paper/1910.06837/) (2019)
- [Federated Learning with Cooperating Devices: A Consensus Approach for Massive IoT Networks](https://mlsanity.com/paper/1912.13163/) (2019)

## Built on by (most cited first, 11 of 11)

- [Transparency and Privacy: The Role of Explainable AI and Federated Learning in Financial Fraud Detection](https://mlsanity.com/paper/2312.13334/) (2023)
- [Real-World Federated Learning in Radiology: Hurdles to overcome and Benefits to gain](https://mlsanity.com/paper/2405.09409/) (2024)
- [The Significance of Machine Learning in Clinical Disease Diagnosis: A Review](https://mlsanity.com/paper/2310.16978/) (2023)
- [Machine and Deep Learning for IoT Security and Privacy: Applications, Challenges, and Future Directions](https://mlsanity.com/paper/2210.13547/) (2022)
- [Decentor-V: Lightweight ML Training on Low-Power RISC-V Edge Devices](https://mlsanity.com/paper/2509.18118/) (2025)
- [Scalability and Performance Evaluation of Federated Learning Frameworks: A Comparative Analysis](https://mlsanity.com/paper/2609.15681/) (2026)
- [Federated Learning on Patient Data for Privacy-Protecting Polycystic Ovary Syndrome Treatment](https://mlsanity.com/paper/2308.11220/) (2023)
- [Towards Robust Federated Image Classification: An Empirical Study of Weight Selection Strategies in Manufacturing](https://mlsanity.com/paper/2408.10024/) (2024)
- [Secure Visual Data Processing via Federated Learning](https://mlsanity.com/paper/2502.06889/) (2025)
- [FedCCL: Federated Clustered Continual Learning Framework for Privacy-focused Energy Forecasting](https://mlsanity.com/paper/2504.20282/) (2025)
- [From Privacy to Trust in the Agentic Era: A Taxonomy of Challenges in Trustworthy Federated Learning Through the Lens of Trust Report 2.0](https://mlsanity.com/paper/2507.15796/) (2025)

## Abstract (arXiv)

Federated learning (FL) is a system in which a central aggregator coordinates the efforts of multiple clients to solve machine learning problems. This setting allows training data to be dispersed in order to protect privacy. The purpose of this paper is to provide an overview of FL systems with a focus on healthcare. FL is evaluated here based on its frameworks, architectures, and applications. It is shown here that FL solves the preceding issues with a shared global deep learning (DL) model via a central aggregator server. This paper examines recent developments and provides a comprehensive list of unresolved issues, inspired by the rapid growth of FL research. In the context of FL, several privacy methods are described, including secure multiparty computation, homomorphic encryption, differential privacy, and stochastic gradient descent. Furthermore, a review of various FL classes, such as horizontal and vertical FL and federated transfer learning, is provided. FL has applications in wireless communication, service recommendation, intelligent medical diagnosis systems, and healthcare, all of which are discussed in this paper. We also present a thorough review of existing FL challenges, such as privacy protection, communication cost, system heterogeneity, and unreliable model upload, followed by future research directions.