Federated Learning (FL) is often celebrated as a privacy-first machine learning technique and for good reasons. Instead of collecting data in one central place, FL allows model training to happen directly on users’ devices, keeping the raw data where it belongs.
But here’s the truth: Federated Learning alone does not guarantee complete privacy.
Yes, it’s a step in the right direction. But model updates shared during FL can still leak sensitive information, especially in adversarial settings or with sophisticated inference attacks.
So how can we truly enable privacy in Federated Learning?
At Idigma, we’ve explored and successfully deployed real-world FL systems that go beyond just local training. In this post, we’ll give you a high-level overview of the three main technologies used to strengthen privacy in FL.
🛡️ 1. Differential Privacy (DP)
Differential Privacy adds calibrated noise to the data or the model updates, making it mathematically provable that an individual user’s data cannot be reverse-engineered from the trained model.
In Federated Learning, DP is often applied during the aggregation step, before the central server sees any updates from client devices.
💡 In short: DP offers formal privacy guarantees by hiding the contribution of any single participant in a sea of noise.
🔒 2. Homomorphic Encryption (HE)
Homomorphic Encryption allows computation directly on encrypted data. This means model updates can be encrypted before they leave the device and the server can still perform aggregation, without ever seeing the raw updates.
HE is extremely promising, but it’s also computationally intensive. However, with recent breakthroughs and hardware acceleration, it’s becoming more practical for FL scenarios.
💡 In short: With FHE, your data stays encrypted from start to finish, even during processing.
🤝 3. Secure Multi-Party Computation (SMPC)
Secure Multi-Party Computation splits sensitive data or computations into multiple pieces, distributed across multiple servers. These parties work together to compute a result, without ever revealing their individual pieces.
In Federated Learning, SMPC is often used to ensure that the aggregation server cannot learn anything from individual client updates, but only the final, combined result.
💡 In short: SMPC enables collaborative computation without exposing private inputs.
🧠 So, How Do They Work with Federated Learning?
Each of these techniques has unique strengths and trade-offs. Depending on the use case, system requirements and privacy guarantees needed, they can be applied alone or in combination.
At Idigma, we’ve successfully built and deployed Federated Learning systems that integrate these advanced privacy-preserving techniques. From secure aggregation using SMPC to differential privacy in sensitive environments, we ensure that privacy isn’t just a checkbox, it’s built into the system’s foundation.
🚀 What’s Next?
In future blog posts, we’ll take a deeper dive into each of these technologies:
- How Differential Privacy balances privacy and utility
- What makes Homomorphic Encryption so powerful (and complex)
- How Secure Multi-Party Computation is reshaping collaborative AI
Stay tuned as we explore the cutting edge of privacy-preserving machine learning.
✨ TL;DR
Federated Learning improves data privacy by design, but it’s not enough on its own. To truly protect users, we need to combine FL with technologies like:
- Differential Privacy (DP)
- Homomorphic Encryption (HE)
- Secure Multi-Party Computation (SMPC)
At Idigma, we specialize in building real, privacy-first, production-ready FL systems powered by these technologies. And this is just the beginning.