Introduction
We live in a data-driven era, surrounded by intelligent devices that help us make better decisions. From smartphones and wearables to autonomous vehicles and smart buildings, the number of Internet of Things (IoT) devices is rapidly growing. These modern distributed networks generate vast amounts of data daily, expanding the power of artificial intelligence (AI) and providing a foundation for training new AI models and applications.
However, traditional machine learning techniques require data to be collected from numerous edge devices and centralized in one location for processing. This centralized approach is inefficient, cost-prohibitive, and unfeasible at scale. It also hinders machine learning productivity and poses significant privacy and security risks.
Enter federated learning—a new gold standard for overcoming these challenges. Embracing federated learning helps safeguard privacy, boost productivity, and unlock the full potential of connected, intelligent devices.
So, what exactly is Federated Learning?
Federated learning, introduced by Google researchers in 2017, represents a paradigm shift in machine learning and AI for IoT. This innovative technique decentralizes model training, allowing businesses and data scientists to train AI models without compromising security.
Federated learning involves moving models to the data rather than moving data to the models. It enables decentralized edge devices or nodes to collaboratively train models without transferring or storing raw data on a central server. Each device trains a local model using its own data and sends only the model updates to a central server, which aggregates them to create a global model. This method ensures data privacy as raw data remains on the local devices.
How Federated Learning Works:
- PRE-INITIALISATION
A central server creates a base model and sends it to Edge MLOps devices or servers.
- LOCAL TRAINING
Each device receives pre-trained or non-trained ML models and trains them with their local data. The devices do not share raw data with each other or with the central server, but the model will now learn and train itself, ensuring data privacy.
- MODEL AGGREGATION
Devices send their locally trained models back to the central server. The server aggregates these local models to build a shared global model, using only the model updates, not the raw data.
- MODEL EVALUATION
Model updates are evaluated to gauge accuracy and identify improvements over the previous version before deployment; as well as to determine the accuracy and identify any improvements over the previous version. These updated are then implemented.
- REPEAT
The shared model process repeats until the model achieves a satisfactory level of accuracy or reaches a predetermined number of iterations.
Advantages of Federated Learning
Federated learning improves security by keeping data on local devices, reducing latency, costs, and productivity bottlenecks inherent in traditional machine learning. It also lowers computational and network costs, increases security and privacy, leverages parallelization, and enhances the resilience of intelligent systems.
Applications in IoT-Rich Industries
Federated learning has wide-ranging applications across various IoT-rich industries, including logistics, manufacturing, energy, healthcare, and automotive. It is particularly valuable where data access is limited by multiparty data ownership, data security, privacy compliance, network data transfer costs, or insufficient data at individual sources.
For example, in industrial settings, federated learning can predict critical events or diagnose malfunctions across diverse devices. While individual data points may be inadequate or generated too infrequently, the collective strength of a federated network enables all devices to benefit from a top-tier model.
The future of IoT Environments
As IoT continues to expand, the volume of data generated at the network edge will skyrocket. Traditional data analysis methods will struggle to keep pace, becoming costlier and less secure. Centralised approaches are increasingly unsuitable for the 5G/6G era due to high communication and storage overheads. Moreover, central data collection poses privacy risks, exposing data to third-party sharing or leaks, violating privacy rights, and compromising security.
Federated learning offers a decentralised solution to these challenges, unlocking the full potential of IoT and paving the way for a secure, efficient future.
