Efficient Secure Aggregation for Private Federated Learning

Core Concepts

The author introduces Fluent, a round-efficient secure aggregation scheme for private federated learning, aiming to reduce communication rounds and latency while ensuring privacy and dropout-robustness.

Abstract

Fluent presents an innovative approach to secure aggregation in federated learning. It minimizes communication rounds, reduces latency, and enhances scalability compared to existing solutions like Bell et al. (CCS 2020) and Ma et al. (SP 2023). The scheme eliminates frequent handshakes, secret sharing operations, and ensures privacy preservation with efficient consistency checks and gradient unmasking. Fluent-Dynamic further enhances system flexibility by introducing participant selection algorithms and alternative secret sharing schemes. Experimental results show significant improvements in computational cost and communication overhead for clients without compromising server performance.

Stats

Single-server secure aggregation schemes encounter practical constraints due to round and communication complexities. Fluent achieves the fewest communication rounds (i.e., two in the collection phase) compared to at least three rounds in existing schemes. Experimental results show that Fluent improves computational cost by at least 75% and communication overhead by at least 25% for normal clients.

Quotes

"Fluent achieves the fewest communication rounds compared to existing schemes." "Experimental results show significant improvements in computational cost and communication overhead."

Key Insights Distilled From

Fluent

by Xincheng Li,... at arxiv.org 03-12-2024

https://arxiv.org/pdf/2403.06143.pdf

Deeper Inquiries

How can the concept of one-time handshake and secret sharing be applied in other areas beyond federated learning

The concept of one-time handshake and secret sharing can be applied in various other areas beyond federated learning to enhance communication efficiency and privacy preservation. One potential application is in secure messaging systems, where users can establish secure communication channels with each other through a one-time handshake for key exchange and secret sharing. This approach can reduce the overhead of repeated handshakes and key exchanges, improving the overall security and performance of the messaging system. Additionally, it can be utilized in IoT (Internet of Things) devices to securely share data among multiple devices without compromising privacy. By implementing one-time handshakes and secret sharing protocols, IoT devices can communicate efficiently while maintaining data confidentiality.

What are potential drawbacks or limitations of reducing communication rounds in secure aggregation schemes

Reducing communication rounds in secure aggregation schemes may introduce certain drawbacks or limitations that need to be considered: Increased Computational Complexity: While reducing communication rounds can improve efficiency, it might lead to an increase in computational complexity on either the client or server side. This additional computational burden could impact the overall performance of the system. Potential Security Risks: Streamlining communication rounds may inadvertently expose vulnerabilities that were mitigated by additional interactions between clients and servers. Simplifying the process could make it easier for malicious actors to exploit weaknesses in the system. Scalability Challenges: In some cases, reducing communication rounds might limit scalability as more participants join the system. The streamlined process may struggle to accommodate a larger number of clients efficiently. Limited Flexibility: A reduction in communication rounds could restrict flexibility in adapting to dynamic changes within the system, such as fluctuating network conditions or varying levels of client participation. It is essential to balance these considerations when optimizing secure aggregation schemes for reduced communication rounds while ensuring robust security measures are maintained.

How might the introduction of dynamic client joining impact the overall efficiency of the system

The introduction of dynamic client joining has several implications for enhancing the overall efficiency of a system: Improved Scalability: Dynamic client joining allows new participants to seamlessly integrate into ongoing processes without disrupting existing operations significantly. This enhances scalability by accommodating fluctuations in participant numbers effectively. 2..Enhanced Flexibility: The ability for clients to join dynamically increases adaptability within the system, enabling quick adjustments based on changing requirements or resource availability. 3..Resource Optimization: Dynamic client joining facilitates better resource utilization by allowing resources (such as decryptors) only when needed rather than having them constantly active throughout all iterations. 4..Reduced Latency: With dynamic client joining algorithms in place, latency issues related to adding new clients are minimized since efficient mechanisms are established for integrating them into ongoing activities swiftly. Overall, incorporating dynamic client joining mechanisms contributes positively towards optimizing efficiency and responsiveness within a distributed computing environment like private federated learning systems."