What is Federated Learning current_medical_grade Federated learning is a machinelearning technique that trains a model across multiple decentralized devices or servers holding local data samples, without exchanging them. This leads to significant advantages in terms of privacy, security and data efficiency and is the approach we favor here.”
What is Federated Learning and How Does it Work?
Model Sharing: An initial model is communicated to the client devices or organization from a central server.
Local Training: The model is trained on the local data of each device or organization, which updates the model parameters.
Model Aggregation: Each device sends back its local updated models to the central server.
Creation of global model update: The central server combines the updates from all devices to obtain an new version of global model.
Distribution of Model: The updated global model is distributed to the devices again and this cycle continues.
Advantages of Federated Learning
Privacy: Federated learning reduces privacy risks by ensuring data remains local.
Secure: Secure as data stays on the devices, not the server, resulting in less data loss.
Data Efficiency: Devices can train on their own (possibly scarce) data, enabling a lot more data-efficient training.
Personalization: Federated learning can build custom models per device or per org.
Use Cases for Federated Learning
Healthcare: Use cases of training models on patient data across two or more hospitals without transferring sensitive patient information.
Mobile Devices: Focusing on device-centric approaches to enhance performance and customization by enabling models to learn based on user data without transmitting it to the cloud.
IoT Devices: Training the models in such a collaborative way to improve its functionality via data received from other IoT devices.
FINANCIAL SERVICES: Preventing fraud and outlier detection in transactions on behalf of customers without exposing secure customer data.
Key Challenges and Future Directions
Although federated learning is a great thing, there are still issues it has to face:
Communication Overhead: This could be a big issue for larger models and larger number of devices for communication between different devices and the central server.Heterogeneity of Device: The different computing power on devices and data quality may cause a negative impact on the training process.
Privacy Breach: Attackers try to extract information from the updates of model.
To overcome these challenges, some researchers are still exploring techniques such as differential privacy, secure aggregation, and homomorphic encryption to make federated learning more privacy-preserving and secure.
Federated Learning: A New Approach Towards Human Centered Following Technology With data privacy and security, federated learning is ready to be the law of amplifying the power of the collaborative nature of our machine learning.
