AI Terminologies 101: Federated Learning – A New Era of Privacy in AI

Federated Learning is a game-changing approach to machine learning, focusing on the decentralisation of data. This approach emphasises privacy preservation, which has become a key concern in the era of Big Data. In this AI Terminologies 101 installment, we delve into the concept of Federated Learning, its workings, and its significance in the modern world.

Federated Learning is essentially a distributed machine learning approach. It enables a model to learn from a vast amount of data distributed across many devices, such as smartphones or IoT devices, without the need to move the data to a central server. This strategy has significant implications for data privacy and security, as it reduces the risk of data breaches and misuse.

The key components of Federated Learning are:

1. Local Model Training: Each device uses its local data to train a model independently. This ensures that sensitive data never leaves the device, enhancing privacy.
2. Model Aggregation: Once local model training is complete, only the model updates (and not the underlying data) are sent to a central server. These updates are aggregated to form a global model.
3. Global Model Distribution: The updated global model is then sent back to all devices, which continue to train the model with their local data. This cycle repeats, progressively improving the model.

Federated Learning has vast potential applications, including:

1. Personalised Recommendations: Federated Learning can enhance personalised recommendation systems, such as those used by online retailers or streaming services, by learning directly from user devices without compromising privacy.
2. Health Care: In healthcare, Federated Learning can be used to develop predictive models using data from various hospitals while maintaining patient confidentiality.
3. IoT Devices: Federated Learning is ideal for IoT devices, where data is often distributed and privacy is crucial.

Federated Learning offers a promising solution to the challenge of learning from large, distributed datasets while preserving data privacy. Its potential applications extend across many domains, including retail, healthcare, and IoT.

In future articles, we’ll dive deeper into other AI terminologies, like Feature Engineering, Transfer Learning, and AutoML. We’ll explain what they are, how they work, and why they’re important. By the end of this series, you’ll have a solid understanding of the key concepts and ideas behind AI, and you’ll be well-equipped to explore this exciting field further.