Federated Learning: Concepts, Challenges and Implementation

doi:10.1111/exsy.70096

Federated Learning: Concepts, Challenges and Implementation

2025-07-08

0

PRE

AI

Muhammad Asghar Khan *

M

Muhammad Attique Khan *

D

David Camacho *

Y

Yasar Abbas Ur Rehman

A

Amir Hussain *

DOI：10.1111/exsy.70096

Original

Original request for help

Save

Abstract

En 中文

Federated Learning (FL) has emerged as an innovative approach for distributed neural networks, allowing multiple clients to collaboratively train a model without centralising their data, thus preserving decentralisation and data privacy. This review provides a comprehensive discussion of FL's core concepts, including its components, key challenges, and distinctions from traditional machine learning. The paper outlines the various types of FL, highlighting applications in privacy-sensitive fields like healthcare and finance. It also addresses recent advancements in self-supervised learning, personalisation, and multi-modal applications within FL, as well as the integration of blockchain technology for enhanced privacy. Key advantages of FL are discussed, such as reduced communication overhead through the transmission of model parameters instead of raw data, which minimises network load and enhances privacy protection. Furthermore, the paper explores emerging questions for FL development, including scalability, fairness, and system standardisation. Real-world examples, such as Google Gboard and brain tumour segmentation, are presented to illustrate FL's practical impact. Finally, the paper discusses future directions, including potential integration with other AI techniques like reinforcement learning and transfer learning. This review provides valuable insights for researchers and professionals who are new to FL or seek a broader understanding of its ecosystem. While there are few studies that explore limited aspect of FL, this review adopts a holistic approach and covers all aspects of FL including foundational concepts, implementation, challenges faced by FL, and real-world implementation. The broader scope, which spans FL from concepts to practical implementation, makes it particularly distinctive and a valuable contribution.

Keywords:

blockchain

cross-device learning

cross-silo learnining

data security

distributed machine learning

Federated Learning

model aggregation

non-IID data

reinforcement learning

self-supervised learning

Federated Learning: Concepts, Challenges and Implementation

Abstract

Journal

Organization