Matchmaking with Federated Learning: The Future of Privacy-Centric Dating Apps - Part I

As data privacy concerns continue to rise, Federated Learning emerges as a approach in the data science and data engineering landscape. Unlike traditional machine learning models that rely on centralized data, Federated Learning allows models to be trained directly at the data source. This not only enhances privacy but also reduces the need for data transfer, making it a more efficient and secure method for collaborative learning.

Now imagine a dating app that learns your preferences and improves its recommendations based on your interactions, all while keeping your data safely on your device.It ensures that sensitive information, such as personal preferences and interactions, remains private, addressing one of the biggest challenges in the online dating industry.

We at Meet7 had taken a use case to create a personalized model that predicts whether a specific user will like a given face based on their individual history, you'll need to adopt a different approach. One way to achieve this is by using a model architecture that incorporates user-specific information. Here are a couple of approaches we had evaluated:

Approach 1: Separate Models for Each User

• Train Individual Models: Train a separate model for each user based on their individual history of likes and dislikes. This means each user will have their own set of weights and biases in the model.
• Advantages: Highly personalized, as each model is tailored to a specific user's preferences.
• Disadvantages: Not scalable for a large number of users, as it requires maintaining and updating a separate model for each user.

Approach 2: Incorporating User Features into a Single Model

• User Embeddings: Represent each user by a unique embedding vector that captures their specific preferences. These embeddings can be learned alongside the model weights during training.
• Model Architecture: Modify your model to accept both the face features and the user embedding as input. The model should learn to combine these inputs to make personalized predictions.
• Example: For a logistic regression model, the input features could be a concatenation of the face features and the user embedding. The model would then learn a separate set of weights for the face features and the user embedding.
• Advantages: Scalable and can handle new users without retraining the entire model.
• Disadvantages: Requires careful design to ensure that the model effectively learns user-specific preferences.

Approach 3: Collaborative Filtering

• Matrix Factorization: Use collaborative filtering techniques like matrix factorization to learn latent features for both users and faces. The interaction between user and face features can be used to predict likes/dislikes.
• Advantages: Well-suited for recommendation systems and can capture complex interactions between users and items (in this case, faces).
• Disadvantages: May require a large amount of interaction data to learn effective representations.

Approach 4: Multi-Task Learning

• Shared Representation: Use a shared neural network to learn a common representation for faces, and then have separate output layers for each user to make personalized predictions.
• Advantages: Leverages shared information between users while allowing for personalization.
• Disadvantages: Can become complex and computationally expensive as the number of users increases.

Among the four approaches discussed, Approach 2: Incorporating User Features into a Single Model and Approach 3: Collaborative Filtering are particularly well-suited for Federated Learning. Here's why:

Approach 2: Incorporating User Features into a Single Model

• User Embeddings: In this approach, user-specific information is encoded as embeddings, which can be learned locally on each user's device. The model updates from each user can be aggregated on the server without needing to share the raw data. This aligns well with the privacy-preserving nature of FL.
• Scalability: Since the model is shared across all users, it is more scalable than having separate models for each user (Approach 1). This makes it more feasible for FL, where managing and aggregating updates from many individual models could be challenging.

Approach 3: Collaborative Filtering

• Matrix Factorization: Techniques like matrix factorization can be adapted for FL by allowing users to compute updates locally based on their interactions with faces. These updates can then be aggregated to improve the global latent feature matrices.
• Privacy-Preservation: Collaborative filtering in FL can be designed to ensure that only the updates to the latent feature matrices are shared with the server, preserving user privacy.

Collaborative filtering typically requires a large dataset to be effective, as it relies on finding patterns across many users' interactions. And thus we decided to use Approach 2.

In the next article we will discuss our experiment on how we used Approach 2 combined with Federated learning to identify curated set of profiles based on facial features to be presented to the user.