Federated Learning and Secure Multi-Party Computation: A Powerful Alliance for Privacy-Preserving AI

In the era of data-driven intelligence, privacy-preserving machine learning has become a cornerstone of responsible AI deployment. Federated Learning (FL) has emerged as a promising paradigm for collaborative model training without centralizing data,. However, its protection mechanisms are not  infallible.  To enhance privacy guarantees, researchers and engineers are increasingly exploring the integration of Secure Multi-Party Computation (SMPC) .

This blog post delves into the synergy between FL and SMPC, how it addresses privacy and security challenges, and the key research frontiers shaping this field.

Federated Learning: The Starting Point

FL enables multiple devices or organizations (e.g., smartphones, hospitals, banks) to collaboratively train a shared machine learning model without transferring their local data. Instead of raw data, participants exchange model updates such as gradients or weights. This design minimizes direct exposure to sensitive information.

However, as we’ve seen in recent research, model updates can still leak information through membership inference attacks, property inference attacks or model inversion or reconstruction attacks.

This vulnerability highlights the need for stronger safeguards beyond local data isolation.

Enter Secure Multi-Party Computation (SMPC)

SMPC is a cryptographic technique that allows multiple parties to jointly compute a function over their inputs without revealing those inputs to each other. The key idea: parties perform computations on encrypted or secret-shared values, and only the final output is revealed.

When applied to FL:

• Model updates (gradients, weights) are secret-shared across multiple parties.
• Aggregation (e.g., summing gradients) is performed using SMPC protocols.
• No single server or entity ever sees the complete, unprotected model update of any participant.

This approach removes the single point of trust in the FL server, and ensures that even if some aggregation servers collude, individual updates remain private.

Benefits of Combining FL + SMPC

The integration of FL with SMPC unlocks a number of advantages that enhance data privacy and system resilience in federated machine learning environments:

• Stronger privacy protection: Neither the central server nor external attackers can infer individual model updates.
• Collusion resistance: SMPC allows for a threshold number of honest servers; privacy is preserved as long as this threshold holds.
• No reliance on adding noise (unlike Differential Privacy) — SMPC can achieve privacy without compromising accuracy (though DP can still be layered on top for extra guarantees).
• Compliance support: The combination aligns well with strict data protection regulations (e.g., GDPR, HIPAA).

Challenges and Research Directions

Despite its potential, the FL + SMPC paradigm still faces several obstacles and open research problems that must be addressed before widespread adoption is feasible:

• Computation and communication overhead: SMPC protocols, especially those for large models, are computationally expensive and require high-bandwidth communication.
• Scalability to massive deployments: Applying SMPC in settings with millions of devices (e.g., edge networks) is still an open research area.
• Heterogeneity handling: SMPC requires synchrony or coordination among participants, which can be challenging with unreliable devices.
• Hybrid solutions: Research is exploring combining SMPC with Differential Privacy or Homomorphic Encryption for layered security.

Conclusion

The union of Federated Learning and Secure Multi-Party Computation represents a major step forward in building AI systems that are not only intelligent but also respectful of individual privacy. As the field evolves, continued advances in cryptographic efficiency, communication protocols, and hybrid privacy techniques will determine how widely these technologies are adopted in real-world systems.

References

Ramanan Mugunthan, Raj Rajaraman, and Murat Kantarcioglu.
“A Survey on Secure Multi-party Computation for Privacy-Preserving Federated Learning.” IEEE Transactions on Services Computing, Early Access, 2021. doi: 10.1109/TSC.2021.3095527

Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth. “Practical Secure Aggregation for Privacy-Preserving Machine Learning.” In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (CCS ’17), pp. 1175–1191. ACM, 2017. doi: 10.1145/3133956.3133982.

Stephen Truex, Ling Liu, Mehmet Emre Gursoy, Lei Yu, and Wenqi Wei.
“A Hybrid Approach to Privacy-Preserving Federated Learning.”
In Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security (AISec ’19), pp. 1–11. ACM, 2019. doi: 10.1145/3338501.3357370.

Trumpet project has received funding from a Research and Innovation action activity under Horizon Europe Framework Programme with Grant Agreement No.101070038