Jul 3 / Kumar Satyam | Rahul Rai

Federated Learning and Data Privacy

What is Federated Learning:

Federated learning (FL) is a decentralized machine learning approach where multiple devices collaboratively train a model without sharing their raw data. Each device uses its data to update the model and then sends these updates to a central server. The server combines these updates to improve the overall model. This method enhances data privacy and security, as sensitive information remains on the local devices. Federated learning is beneficial when data is distributed across many users, such as in mobile applications, healthcare, and IoT devices, enabling advanced analytics while respecting user privacy and regulatory constraints. 
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Federated Learning Applications:

1. Internet of Things (IoT): 

Federated learning is applied in the IoT ecosystem, where numerous smart devices collect and process data in a collaborative effort. For example, smart home devices like security cameras and voice assistants can work together to improve their skills in recognizing voices, predicting activities, and managing energy. They do this by sharing only the updates to their learning models, not the data they collect. This collaborative approach ensures that user data collected by individual devices remains local, thereby protecting privacy while enabling more intelligent and more efficient IoT systems.

2. Healthcare:

In healthcare, federated learning allows hospitals to work together to train models that can predict disease outbreaks or patient readmissions without sharing their patients' private data. Each hospital uses its data to improve the model and then shares only the improvements, not the actual data. This way, the model benefits from a wide variety of data from different hospitals, making it more accurate. For example, a federated learning model trained across several hospitals can better predict patient outcomes for rare diseases due to the more extensive and varied training dataset. This approach helps maintain patient confidentiality and comply with HIPAA regulations (Health Insurance Portability and Accountability Act).

3. Mobile Application:

Google's Gboard is a prominent example of federated learning in mobile applications. Gboard is a virtual keyboard app that uses federated learning to improve text predictions and personalized suggestions without uploading users' typing data to central servers. Instead, the app processes the data on your device and only sends updates to improve the model. These updates are combined to make the overall model more accurate, improving your experience while keeping your typing data private.

How to implement secure federated learning?

  • Data Preparation and Distribution: Each participating device or node prepares and preprocesses its local data for training. The data stays on the device, and only the model updates are shared, ensuring privacy and security.
  • Model Initialization: An initial global model is distributed to all devices participating in federated learning. This model is the foundation for local training on each device, ensuring consistency across all devices and facilitating collaborative learning without sharing raw data.
  • Local Training: Each device trains the global model on its local data, producing local model updates. These updates usually consist of model parameters or gradients from the training process. These are then shared with the central server for aggregation, enhancing the global model without sharing raw data.
  • Encryption of Model Updates: Local model updates are encrypted before transmission to the central server to enhance security. Techniques such as homomorphic encryption enable computation on encrypted data. At the same time, Secure Multi-Party Computation (SMPC) allows encrypted updates to be aggregated without revealing their values, ensuring privacy during federated learning.
  • Global Model Update: The aggregated, encrypted updates are decrypted if needed and utilized to update the global model. This updated model is then distributed back to participating devices for the subsequent round of training, ensuring continual improvement while preserving data privacy in federated learning.
  • Blockchain for Transparency and Integrity: Integrating blockchain bolsters process integrity by recording each model update and aggregation step on a decentralized ledger. This ensures transparency and prevents tampering, providing a reliable and immutable record of the federated learning process and enhancing trust and security.
  • Iterative Process: This process is repeated iteratively, with each round of local training, encryption, secure aggregation, and global model update. The model is updated and improved with each iteration, gradually converging or achieving the desired performance.

Data encryption techniques in federated learning:

Data encryption techniques play a significant role in federated learning, ensuring the security and privacy of the data involved.

1. Homomorphic Encryption:

Homomorphic encryption allows computations on encrypted data without decrypting it first. In federated learning, each device can encrypt its model updates before sending them to the central server. The server then combines these encrypted updates and returns the combined result to the devices. The devices can then decrypt the aggregated result to update their local models. This technique ensures that the central server never has access to the raw data or intermediate computations, significantly enhancing data privacy.

2. Secure Multi-Party Computation (SMPC): 

Secure multi-party computation (SMPC) lets several parties work together to compute a result without revealing their inputs. In the context of federated learning, SMPC can be used to ensure that the aggregation of model updates is done in a way that the individual updates are not revealed. Each device encrypts its updates with a shared secret and sends them to the central server. The server combines these encrypted updates without knowing what they are. Then, the final combined result is sent back and decrypted by the devices. This way, individual updates stay private while contributing to the overall model improvement.

3. Differential Privacy: 

Differential privacy adds random noise to the data or model updates before sharing. This technique ensures that the inclusion or exclusion of a single data point does not significantly affect the output, thereby protecting individual privacy. In federated learning, each device adds this noise to its updates before sending them to the central server. This makes it difficult for any observer, including the central server, to infer any specific data point from the aggregated updates.

4. Federated Averaging with Encryption: 

The central server calculates the average of updates from different devices in federated averaging. To enhance privacy, devices can encrypt their model updates before sending them to the central server. The server performs secure aggregation on these encrypted updates to compute the average. One way to do this is by using homomorphic encryption, which allows the server to work directly with the encrypted values. This ensures privacy while still improving the overall model.

5. Blockchain for Secure Aggregation: 

Blockchain technology can help ensure that the aggregation process in federated learning is secure and trustworthy. It does this by recording all updates and their combination in a decentralized database. This record cannot be changed or tampered with, providing a precise and reliable way to confirm that the updates were aggregated correctly. Notably, while the blockchain keeps track of the process, it doesn't reveal the updates, preserving privacy.

Federated Learning vs. centralized machine learning for privacy

I. Data Distribution: 

In classical machine learning, data is assumed to be independently and identically distributed across participants. In contrast, federated learning assumes non-identically distributed data since users have different data types. Data is evenly distributed among participants, which is impractical in real scenarios where participant numbers vary. Therefore, federated learning divides the data into shards, ensuring each participant receives equal information and accommodating the variability in the number of participants and their data types.

II. Continual Learning: 

In classical machine learning, a central model is trained using all available data in a centralized setting. However, when quick responses are needed, communication delays between user devices and the central server can hinder user experience. While federated learning can run on user devices, continuous learning becomes challenging as models typically require access to the complete dataset, which isn't available locally. This discrepancy between the need for fast responses and the difficulty of continuous learning on user devices poses a significant challenge for federated learning implementation.

III. Data Privacy: 

Federated learning addresses privacy risks by allowing local model training on user devices, eliminating the need to share data with a central server. Unlike classical machine learning, where training occurs on a single server, federated learning enables cooperative model training on decentralized data. This approach ensures continuous learning without exposing sensitive data to the cloud server, enhancing privacy.

IV. Aggregation of data sets: 

Classical machine learning centralizes user data, risking privacy violations and data breaches. Federated learning upgrades models continuously, incorporating client input without aggregating data, preserving privacy and security. In the future, AI and ML, particularly in customer service, promise scalable systems, on-the-go model creation, and precise, timely results, revolutionizing business applications.

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