# Federated Learning

## Overview

Federated learning is a machine learning technique that allows models to be trained on decentralized data. This is particularly useful in the financial industry, where data is often sensitive and cannot be centralized.

## Potential Applications

Federated learning could be used in Adam to:

*   Train models on data from multiple financial institutions without centralizing the data.
*   Improve the accuracy of models by leveraging a larger and more diverse dataset.
*   Preserve the privacy and security of sensitive financial data.

## Challenges

*   **Communication overhead:** Federated learning can be communication-intensive, as it requires frequent communication between the central server and the clients.
*   **Data heterogeneity:** The data on different clients may be heterogeneous, which can make it difficult to train a single global model.
*   **Security:** Federated learning is vulnerable to a variety of security attacks, such as model poisoning and data inference attacks.