Is Federated Learning the future?

What is Federated Learning?

In today's world, the importance of data privacy and security cannot be overstated. With the exponential growth of connected devices and the ever-increasing volumes of data they generate, traditional centralised machine learning approaches face significant challenges. Enter federated learning—a revolutionary paradigm that promises to reshape the landscape of artificial intelligence (AI) by enabling collaborative model training without compromising data privacy.

Federated learning is a decentralised approach where multiple devices or servers collaboratively train a shared model while keeping the data localised. This innovative technique allows organisations and individuals to harness the collective intelligence of distributed data sources without the need to transfer sensitive information to a central server. Imagine hospitals around the world collaboratively training a medical AI model on patient data without ever sharing the sensitive information. The potential for breakthroughs in healthcare, finance, and beyond is immense.

But how exactly does federated learning work, and what makes it such a game-changer in the realm of AI? We can delve into the fundamentals of federated learning, explore its numerous benefits whilst addressing the challenges that come with this groundbreaking technology.

Definition

Federated learning is a decentralised machine learning approach where multiple devices or servers collaboratively train a shared model without exchanging their local data. Instead of sending raw data to a central server, each device computes model updates (gradients) based on its local data and then sends these updates to a central server. The server aggregates these updates to improve the global model, which is then shared back with the devices. This process continues iteratively, allowing the model to learn from distributed data while preserving privacy and reducing data transfer.

In essence, federated learning enables collaborative learning while keeping data localised, ensuring data privacy and security.

How it works

Data Stays Local

In FL, data remains on individual devices instead of being centralised. This means your personal data never leaves your device, maintaining privacy and security.

Local Training

Each device trains a copy of the global model using its local data. For instance, your smartphone might improve its predictive text model based on your messages, without sharing any content.

 Sharing Model Updates

Devices send only model updates (gradients) back to a central server, not the raw data. This ensures privacy while still contributing to the model's improvement.

Aggregation

The central server aggregates updates from all devices to refine the global model. Techniques like Federated Averaging combine these updates to enhance the model.

Iteration

The improved model is sent back to devices, and the process repeats. This iterative cycle allows the model to get better while keeping data private. 

Benefits

Data Protection

Federated learning offers significant advantages in terms of data protection and privacy. By keeping data on local devices, FL ensures that sensitive information never leaves its source. This decentralised approach means that personal and proprietary data remains secure, minimising the risk of data breaches and unauthorised access. It complies with stringent data protection regulations, such as GDPR, by ensuring that raw data does not leave the network, thereby preserving user privacy.

Limiting Data Transfer

Another key benefit of federated learning is the reduction in data transfer. Traditional machine learning methods require transferring large amounts of data to a central server, which can be costly and time-consuming. In contrast, FL only sends model updates, significantly reducing the bandwidth and computational resources needed for data transmission. This efficiency makes it particularly suitable for edge devices and IoT applications, where network connectivity may be limited or expensive.

Improved models and collaboration

Federated learning enables the creation of improved models through collaborative efforts. By leveraging the diverse data distributed across various devices, FL can capture a wider range of patterns and behaviours than a single centralised dataset could. This leads to more robust and generalised models. Moreover, FL fosters collaboration between different organisations and entities, allowing them to collectively train AI models without sharing sensitive data, therefore accelerating innovation and advancements across industries.

Use cases

SAFERAI

At Raiven we are using federated learning to transform the manufacturing industry, particularly in health and safety risk prediction, through initiatives like SAFER AI (Safety Advancing Federated Estimation of Risk using Artificial Intelligence - SAFER AI). We collaborate with P&G to train a shared AI model that predicts risk in a manufacturing environment, thus improving workplace safety without sharing sensitive operational data. Other companies can train the model on their own local safety data, then sends model updates to a central server. These updates are aggregated to enhance the global model, ensuring robust predictions across diverse environments. This approach enables companies to benefit from collective insights, enhancing safety standards while maintaining data privacy and protecting proprietary information. SAFERAI exemplifies how federated learning fosters a safer and more secure industrial landscape.