Federated Learning is a decentralized way to train AI and ML models without anyone seeing or touching your data. Normally when we train data, it is aggregated from several edge devices such as mobile phones, laptops, etc. and is brought through to one centralized server. However, these techniques pose privacy concerns for user-sensitive and personalized data. Centralized servers store sensitive data that are vulnerable to security breaches so if a server is compromised, it could lead to unauthorized access and potential misuse of personal information. Here’s a basic overview of how federated learning works and its role in privacy:
1. Decentralized Training:
Instead of sending raw data to a central server for model training, Federated Learning enables training to occur locally on individual devices or servers.
2. Model Distribution:
A global model is initially created and sent to all participating devices. Each device then trains the model on its local data without sharing the actual data.
3. Model Updates:
After local training, only the model updates (changes in model parameters) are dispatched to devices trained locally and only the resultant model updates are sent back to the server.
4. Aggregation:
The central server collects the model updates from all devices and aggregates them to improve the global model. This process iterates, refining the model with each round of updates.
Federated learning is a versatile approach that can be applied to train various types of machine learning models. The key advantage is that it enables model training across decentralized devices or servers while keeping raw data localized. Some other advantages are:
Data Privacy: Since raw data never leaves individual devices, user-sensitive information is kept private. Only model updates, which are typically anonymized and aggregated, are shared.
Reduced Centralized Risk: Centralized servers do not store individual user data, reducing the risk of data breaches or unauthorized access.
User Control: Users have more control over their data as training occurs locally on their devices. They can choose to participate or opt-out.
Efficient Learning: Federated Learning is often more efficient as it allows models to be trained on distributed datasets without the need to transfer large amounts of data centrally.
However, despite the upsides in enhancing privacy, it’s not a silver bullet solution. Transmitting large amounts of data between client devices and a central server can incur significant communication overhead, especially in scenarios where bandwidth is limited. The flexibility of federated learning makes it suitable for a wide range of machine learning applications, particularly in scenarios where privacy concerns are paramount. It allows collaborative model training while respecting the privacy of data on individual devices.
There are a lot of privacy concerns with the growing number of machine learning and AI models being built right now and for good reason too. It feels like an arms race on finding the best sources of accurate and representable data. And this applies to any machine learning model. Finding good sources of accurate and representative data is one of the most important factors in building good models. Addressing these sorts of challenges involve a combination of careful data preprocessing, data augmentation, and techniques to mitigate biases.
While federated learning provides privacy-preserving approach to collaborative model training, it also requires addressing challenges related to non-IID (non-identically distributed) data across devices. Non-IID data refers to a scenario where the data across different devices or groups does not follow an identical distribution. In other words, the characteristics, patterns, or statistical properties of the data vary among the individual devices or subgroups, which poses challenges for machine learning models that assume a consistent distribution.
There are three main types of federated learning which include:
Horizontal Federated Learning: Devices have the same features but different instances of data
Vertical Federated Learning: Devices have different features but share common instances of data
Federated Transfer Learning: Combines federated learning with transfer learning, adapting a pretrained model to local data.
Federated Learning is still relatively new and like any training of a model, poses it’s own design and deployment challenges. Of course, there are many other techniques of ML and AI training depending on use-case. Federated learning is best when data privacy, decentralization, collaboration, and dynamic data are priorities. For scenarios with centralized and homogenous datasets, less concern about data privacy, or simpler model requirements, traditional machine learning approaches may be the best approach. Ultimately, the choice depends on the unique characteristics and objectives of the application.