What is Federated Learning?
Federated Learning (FL) is a machine learning approach where a model is trained across multiple decentralized devices or servers holding local data samples, without exchanging them.
This contrasts with traditional centralized machine learning techniques where all data is uploaded to one server, as FL stores data on the device, decoupling the ability to do machine learning from the need to store the data in the cloud. In this article, we’ll explore how to implement federated learning.
Benefits of Federated Learning
- Privacy Preservation: By keeping data on local devices, FL ensures that personal and sensitive information remains private and secure.
- Reduced Latency: Training models locally reduces the need for extensive data transfer, thus decreasing latency.
- Scalability: FL allows for the inclusion of a large number of devices, each contributing to the training process without overwhelming a central server.
- Personalization: Models can be customized for individual users or specific environments, increasing the relevance and accuracy of predictions.
How Federated Learning Works
Federated Learning is particularly beneficial for privacy-sensitive applications like healthcare, finance, and personal devices, where raw data sharing is impractical or legally restricted. The basic steps in federated learning are:
- Initialization: The server initializes the model.
- Selection: A subset of devices is selected to participate in training.
- Configuration: Devices configure their local training environment.
- Local Training: Each device trains the model on its local data.
- Aggregation: The server collects the locally trained models and aggregates them.
- Update: The aggregated model is distributed back to the devices.
Federated learning operates by maintaining a generic baseline model at a central server. This model is copied and distributed to client devices, which then train these copies using their local data. As a result, the models on individual devices become increasingly personalized, enhancing the user experience.
In the subsequent phase, updates (model parameters) from the locally trained models are sent back to the central server using secure aggregation techniques. The central server combines and averages these updates to generate new insights. Since the data comes from different sources, the model can become more generalizable.
Once the central model is updated with the new parameters, it is redistributed to the client devices for another round of training. With each cycle, the models gather a broader range of information and improve continuously without compromising privacy.
How does federated learning work?
Source: Medium
Federated Learning Strategies
Centralized Federated Learning
Centralized federated learning relies on a central server to coordinate the process. This server selects the client devices initially and collects model updates during training. Communication occurs solely between the central server and the individual edge devices.
While this approach is straightforward and capable of generating accurate models, it presents a bottleneck issue. Network failures at the central server can disrupt the entire process, leading to potential downtime and inefficiencies.
Centralized federated learning
Source: Google Research
Decentralized Federated Learning
Decentralized federated learning eliminates the need for a central server. Instead, model updates are shared directly among interconnected edge devices. The final model is obtained by aggregating the local updates from these devices.
This approach mitigates the risk of a single point of failure. However, the accuracy of the model heavily depends on the network topology of the edge devices.
Decentralized federated learning
Source: ResearchGate
Heterogeneous Federated Learning
Heterogeneous federated learning involves a diverse array of clients, such as mobile phones, computers, and IoT (Internet of Things) devices. These devices can vary significantly in hardware, software, computational capabilities, and data types.
HeteroFL was developed to address the limitations of traditional federated learning strategies, which often assume that local models are similar to the central model. In reality, this is seldom the case. HeteroFL allows a single global model to be trained by combining insights from several different local models, thereby accommodating the diversity of client devices.
Heterogeneous federated learning
Source: SDLab
Federated Learning Frameworks
As computer vision research advances with large-scale Convolutional Neural Networks (CNNs) and dense transformer models, a gap in tools and methods for implementing them in a federated environment has become apparent.
The FedCV framework aims to bridge this gap, facilitating the transition from research to real-world implementation of federated learning algorithms.
FedCV is a comprehensive library designed for federated learning in computer vision applications, including image segmentation, image classification, and object detection. It provides easy access to various datasets and models through user-friendly APIs. The framework consists of three main modules: Computer Vision Applications Layer High-Level API Low-Level API Let’s explore the contributions of each of these modules.
FedCV framework
Source: FedCV
The High-Level API
The high-level API in FedCV offers models for computer vision tasks such as image segmentation, image classification, and object detection. Users can utilize existing data loaders and data partitioning schemes, or create their own non-i.i.d. (non-identical and independent distribution) datasets to test the robustness of federated learning methods, reflecting real-world data characteristics.
This API also includes implementations of state-of-the-art federated learning algorithms like FedAvg, FedNAS, and more. With support for distributed training across multiple GPUs, these algorithms can be trained efficiently. Additionally, novel distributed computing strategies enhance the training process.
The user-oriented design of the API enables easy implementation and flexible interactions between clients and workers.
The Low-Level API
The low-level API focuses on enhanced security and privacy, offering modules that ensure secure and private communication between servers located in different regions.
Implementing Federated Learning
Step 1: Setting Up the Environment
To implement FL, we will use TensorFlow Federated (TFF), a framework specifically designed for federated learning.
Install TensorFlow and TensorFlow Federated:
pip install tensorflow tensorflow-federated
Step 2: Define the Model
We will define a simple neural network model using TensorFlow.
import tensorflow as tf def create_keras_model(): return tf.keras.models.Sequential([ tf.keras.layers.Input(shape=(784,)), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dense(10, activation='softmax') ])
Step 3: Prepare the Data
For demonstration, we’ll use the MNIST dataset. In real-world applications, data would be distributed across different clients.
import tensorflow_federated as tff
def preprocess(dataset):
def batch_format_fn(element):
return (tf.reshape(element['pixels'], [-1, 784]),
tf.reshape(element['label'], [-1, 1]))
return dataset.repeat(10).shuffle(1000).batch(20).map(batch_format_fn).prefetch(10)
mnist_train, mnist_test = tf.keras.datasets.mnist.load_data()
def get_data_for_client(client_id, mnist_data):
client_data = {
'pixels': mnist_data[0][client_id * 6000:(client_id + 1) * 6000],
'label': mnist_data[1][client_id * 6000:(client_id + 1) * 6000]
}
return tf.data.Dataset.from_tensor_slices(client_data).map(lambda x, y: {'pixels': x, 'label': y})
clients_data = [get_data_for_client(i, mnist_train) for i in range(10)]
clients_data = [preprocess(client) for client in clients_data]
Step 4: Federated Learning Process
iterative_process = tff.learning.build_federated_averaging_process(
model_fn=create_keras_model,
client_optimizer_fn=lambda: tf.keras.optimizers.SGD(learning_rate=0.02),
server_optimizer_fn=lambda: tf.keras.optimizers.SGD(learning_rate=1.0)
)
state = iterative_process.initialize()
for round_num in range(1, 11):
state, metrics = iterative_process.next(state, clients_data)
print(f'Round {round_num}, Metrics={metrics}')
Real-Life Applications of Federated Learning
Federated learning is already being utilized across various industries and use cases. Here are some of the most common applications:
Smartphones
Smartphones are a prevalent platform for federated learning. Examples include:
- Word Prediction: Predictive text in keyboards.
- Face Recognition: Logging into devices using facial recognition.
- Voice Recognition: Enhancements for virtual assistants like Siri or Google Assistant.
Federated learning personalizes the user experience while maintaining privacy by keeping data on the device.
Transportation
Self-driving cars leverage computer vision and machine learning to analyze their surroundings and make real-time decisions. Continuous adaptation to different environments requires learning from diverse datasets to improve accuracy.
Federated learning accelerates this process by allowing models to learn locally on each vehicle, reducing reliance on cloud-based approaches that can introduce latency and slow down the system.
In summary, federated learning enhances the robustness and efficiency of models in real-world applications while preserving data privacy and security.
Road object detection for traffic flow analysis
Source: Roboflow
Manufacturing
In the manufacturing sector, federated learning can enhance various processes by leveraging broader data sets. For example:
- Product Recommendation Systems: Traditionally, product demand is assessed based on individual sales data. Federated learning can improve recommendation systems by integrating a wider range of data sources, leading to more accurate and personalized recommendations.
- Augmented Reality (AR) / Virtual Reality (VR): These technologies are used for object detection and remote operations, including virtual assembly. Federated learning can refine detection systems and help develop optimal models for AR/VR applications.
- Industrial Environment Monitoring: Federated learning enables efficient time-series analysis of environmental factors collected from multiple sensors across different companies. This approach maintains data privacy while aggregating insights from various sources to improve monitoring and predictive capabilities.
In summary, federated learning enhances manufacturing by improving recommendation systems, advancing AR/VR technologies, and optimizing industrial environment monitoring, all while preserving data privacy.
Healthcare
The sensitive nature of healthcare data and its restricted access due to privacy issues make it difficult to scale machine learning systems in this industry globally.
With federated learning, models can be trained through secure access to data from patients and medical institutions while the data remains at its original premises. It can help individual institutions collaborate with others and makes it possible for the models to learn from more datasets securely.
Additionally, federated learning can allow clinicians to gain insights about patients or diseases from wider demographic areas beyond local institutions and grant smaller rural hospitals access to advanced AI technologies.
Federated learning in healthcare
Source: Nvidia
Key Takeaways
Federated Learning offers a promising approach to decentralized data training, ensuring privacy and regulatory compliance while enabling collaborative model building.
Federated Learning (FL) enables the development of more accurate and generalizable models while keeping data securely on client devices.
FL typically employs three main strategies: Centralized FL, Decentralized FL, and Heterogeneous FL. Popular algorithms within these strategies include FedSGD, FedAvg, and FedDyn.
FedCV is a specialized FL framework designed for computer vision applications. It addresses the gap between research and practical implementation by offering a unified, user-friendly library with a range of functionalities. FedCV’s practical applications span multiple industries, including healthcare, transportation, and manufacturing.
