Secure Stateful Aggregation: A Practical Protocol with Applications in Differentially-Private Federated Learning

• URL: http://arxiv.org/abs/2410.11368v1
• Date: Tue, 15 Oct 2024 07:45:18 GMT
• Title: Secure Stateful Aggregation: A Practical Protocol with Applications in Differentially-Private Federated Learning
• Authors: Marshall Ball, James Bell-Clark, Adria Gascon, Peter Kairouz, Sewoong Oh, Zhiye Xie,
• Abstract summary: DP-FTRL based approaches have already seen widespread deployment in industry. We introduce secure stateful aggregation: a simple append-only data structure that allows for the private storage of aggregate values. We observe that secure stateful aggregation suffices for realizing DP-FTRL-based private federated learning.
• Score: 36.42916779389165
• License:
• Abstract: Recent advances in differentially private federated learning (DPFL) algorithms have found that using correlated noise across the rounds of federated learning (DP-FTRL) yields provably and empirically better accuracy than using independent noise (DP-SGD). While DP-SGD is well-suited to federated learning with a single untrusted central server using lightweight secure aggregation protocols, secure aggregation is not conducive to implementing modern DP-FTRL techniques without assuming a trusted central server. DP-FTRL based approaches have already seen widespread deployment in industry, albeit with a trusted central curator who provides and applies the correlated noise. To realize a fully private, single untrusted server DP-FTRL federated learning protocol, we introduce secure stateful aggregation: a simple append-only data structure that allows for the private storage of aggregate values and reading linear functions of the aggregates. Assuming Ring Learning with Errors, we provide a lightweight and scalable realization of this protocol for high-dimensional data in a new security/resource model, Federated MPC : where a powerful persistent server interacts with weak, ephemeral clients. We observe that secure stateful aggregation suffices for realizing DP-FTRL-based private federated learning: improving DPFL utility guarantees over the state of the art while maintaining privacy with an untrusted central party. Our approach has minimal overhead relative to existing techniques which do not yield comparable utility. The secure stateful aggregation primitive and the federated MPC paradigm may be of interest for other practical applications.

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