Practical Secure Aggregation for Federated Learning on User-Held Data (1611.04482v1)

Abstract: Secure Aggregation protocols allow a collection of mutually distrust parties, each holding a private value, to collaboratively compute the sum of those values without revealing the values themselves. We consider training a deep neural network in the Federated Learning model, using distributed stochastic gradient descent across user-held training data on mobile devices, wherein Secure Aggregation protects each user's model gradient. We design a novel, communication-efficient Secure Aggregation protocol for high-dimensional data that tolerates up to 1/3 users failing to complete the protocol. For 16-bit input values, our protocol offers 1.73x communication expansion for $2^{10}$ users and $2^{20}$-dimensional vectors, and 1.98x expansion for $2^{14}$ users and $2^{24}$ dimensional vectors.

• The paper introduces a novel secure aggregation protocol that tolerates up to 33% user dropout while ensuring efficient model updates.
• It employs iterative refinements such as one-time pad perturbation, secret sharing, and double masking to robustly protect user-held data.
• Empirical results show communication expansions as low as 1.73× for 2^10 users, indicating its strong practical potential for real-world applications.

Practical Secure Aggregation for Federated Learning on User-Held Data

Secure Aggregation is an essential cryptographic protocol within the context of Federated Learning (FL), particularly when considering privacy-preserving strategies for training deep neural networks using user-held data. The paper presents a novel secure aggregation protocol aimed at enhancing the efficiency and robustness of federated learning while simultaneously preserving user privacy. This essay explores the technical contributions of this work and its implications for the field.

Efficiency and Robustness in Federated Learning

The authors address the constraints of user devices, which include limited computational power and sporadic connectivity, by proposing a communication-efficient secure aggregation protocol. This protocol allows users to contribute their model gradients without revealing any private data to a central entity. Notably, the protocol tolerates up to a third of the participating users dropping out, which is a significant improvement over many existing solutions.

For the scenarios considered, the protocol achieves communication expansions of $1.73 \times$ for $2^{10}$ users with $2^{20}$-dimensional vectors and $1.98 \times$ for $2^{14}$ users with $2^{24}$-dimensional vectors. These results are indicative of the protocol's practicality for high-dimensional data, making it suitable for real-world federated learning applications.

Protocol Overview

The secure aggregation protocol is built using a series of refinements, beginning with a basic model of input perturbation and evolving to address robustness and security concerns. Initially, it proposes a simple mechanism where each user perturbs their input using a one-time pad shared with every other user. The server, upon receiving all perturbed inputs, can compute the aggregate result while individual private inputs remain concealed. However, this initial approach lacks robustness to user dropout.

Subsequent iterations of the protocol integrate secret sharing to ensure that the system remains functional even if some users do not complete the protocol. The use of double masking enhances robustness against malicious servers. Protocol 4, the final version, addresses practical deployment issues arising from the lack of direct secure communication between user devices. Key agreement is bootstrapped in a server-mediated environment, establishing shared secrets used for masking.

Implications and Future Directions

This paper's contributions have significant implications for federated learning systems, especially in scenarios that require rigorous privacy guarantees. The proposed secure aggregation mechanism reduces the dependency on a trusted server while managing communication costs efficiently. This is particularly relevant for pervasive mobile applications where privacy risks remain high, and bandwidth is often limited.

Theoretically, the work opens pathways for further exploration of secure multi-party computation techniques in decentralized machine learning. The enhancement of robustness against dropout and the diminishment of server trust requirements represent substantial progress in applying privacy-preserving technologies in distributed AI systems.

Future research could focus on integrating this protocol into larger, more complex federated learning ecosystems, potentially incorporating differential privacy features and exploring multilayer security against more sophisticated adversarial models. Additionally, extending the scalability of the protocol to handle even larger dimensions and further reducing communication overhead would be of paramount importance to advancing federated learning adoption in diverse sectors.

In conclusion, the paper makes a pivotal contribution to federated learning by developing a robust and efficient secure aggregation protocol, setting a foundation for sustainable and privacy-preserving AI deployment.