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Federated Reinforcement Learning in IoT

Google researchers initially presented federated learning in 2017.

Federated learning is a paradigm shift in artificial intelligence and machine learning for the Internet of Things that addresses issues with conventional approaches.

It is a revolutionary method of training AI models for enterprises and data scientists without compromising security because of its decentralized approach to model training. However, this is only one benefit of federated learning. Let’s examine federated learning in more detail and see how it benefits data scientists who operate in Internet of Things settings.

Exactly, What Is Federated Learning?

“Federated learning” is a Machine Learning (ML) approach that transfers models to the data rather than vice versa.¹

Federated learning (FL) is a cutting-edge machine learning technology that allows decentralized edge devices or nodes to train models cooperatively without uploading or storing raw data on a central server.

Instead, each device trains an individual model on its data, and only model changes are communicated to a centralized server, which combines them to form a global model. This strategy preserves data privacy because the raw data remains on local devices.

When applied to IoT, FL increases security, decreases delay, costs, and productivity constraints in classical machine learning, and lowers model generation costs by lowering data transmission needs and CPU expenses in the central server.

How FL Functions

1. Pre-initialization

A base model is created by a central server and distributed to Edge MLOps devices or servers.

2. Local Training

Each device gets pre-trained or untrained machine learning models and trains them using local data. The devices do not exchange raw data with one another or with the central server, but the model is now learning and training on its own.

3. Integration of Models

Devices transmit locally learned models to the centralized server, which integrates the local models to create a shared global model. The devices then communicate back to the central server their locally learned models. After that, the server combines the local models to create a shared global model. Take note that only the model results are displayed, not the data.

4. Evaluation of Models

Before deployment, model changes are assessed to determine correctness and discover improvements over the prior version. The model changes are assessed for correctness and any advancements over the prior version before being applied.

5. Repetition

The shared model method is repeated until the model reaches an appropriate degree of accuracy or a set number of repetitions.

The Advantages of Federated Learning in IoT

1. Protects user data confidentiality

Federated learning reduces personal data exposure concerns by storing raw data on devices during training and only sends model updates to the central server.

2. Model Quality has been improved

Devices can work together to train high-quality models from a wider range of data without disclosing personal information. Regular local model updates enable edge devices to attain levels of performance that exceed their particular capabilities.

3. Flexible Scalability

Federated learning uses the computing capacity of several IoT devices in different places at the same time, increasing scalability without taxing a centralized server. The absence of raw data transmission lowers communication costs even further, particularly in low-bandwidth IoT networks.

Disadvantages of Federated Learning in IoT

1. Limited Computing Resources: IoT devices frequently have limited processing power, memory, and battery life, rendering computation-heavy FL algorithms difficult to perform.

2. Communication Bandwidth Obstacles: FL relies on tiny data transfers across the central server and edge devices; however, IoT devices may have limited connectivity bandwidth, hindering data delivery.

3. Connectivity to the network: IoT devices are frequently deployed in locations with limited or inconsistent network access. This can make it difficult to keep the devices linked to the central server, which is required for federated learning to work.


To conclude, federated learning greatly increases the scalability of training models by decreasing computing and network costs, boosting security and privacy, leveraging parallelization, and increasing the adaptability of intelligent systems. This is accomplished by delivering learning to the device and allowing for aggregation over a networked population of devices.