SAFE: Secure Aggregation with Failover and Encryption

We propose, analyze, and experimentally evaluate a novel secure aggregation algorithm targeted at cross-organizational federated learning applications with a fixed set of participating learners. Our solution organizes learners in a chain and encrypts all traffic to reduce the controller of the aggregation to a mere message broker. We show that our algorithm scales better and is less resource demanding than existing solutions, while being easy to implement on constrained platforms. With 36 nodes, our method outperforms state-of-the-art secure aggregation by 70x, and 56x with and without failover, respectively.

Authors

Publication Details

Journal
ACM Transactions on Modeling and Performance Evaluation of Computing Systems
Published
2025-02-07
DOI
https://doi.org/10.1145/3716630
Citations
6
Primary Topic
Privacy-Preserving Technologies in Data
Type
article
Field-Weighted Citation Impact
7.44
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article

SAFE: Secure Aggregation with Failover and Encryption

Thomas Sandholm, Sayandev Mukherjee, Bernardo A. Huberman
6 citations
ACM Transactions on Modeling and Performance Evaluation of Computing Systems
Privacy-Preserving Technologies in Data
7.44
article

SAFE: Secure Aggregation with Failover and Encryption

Thomas Sandholm, Sayandev Mukherjee, Bernardo A. Huberman
article en
6 citations

Abstract

We propose, analyze, and experimentally evaluate a novel secure aggregation algorithm targeted at cross-organizational federated learning applications with a fixed set of participating learners. Our solution organizes learners in a chain and encrypts all traffic to reduce the controller of the aggregation to a mere message broker. We show that our algorithm scales better and is less resource demanding than existing solutions, while being easy to implement on constrained platforms. With 36 nodes, our method outperforms state-of-the-art secure aggregation by 70x, and 56x with and without failover, respectively.

ACM Transactions on Modeling and Performance Evaluation of Computing SystemsVol. 10(1)
Openalex Percentile: Top 4%
Privacy-Preserving Technologies in Data
7.44
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SAFE: Secure Aggregation with Failover and Encryption — Thomas Sandholm, Sayandev Mukherjee, et al. · ACM Transactions on Modeling and Performance Evaluation of Computing Systems (2025) | TGRS Research Map | TGRS