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SAFE: Secure Aggregation with Failover and Encryption

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We propose, analyze, and experimentally evaluate a novel secure aggregation algorithm targeted at crossorganizationalfederated learning applications with a fixed set of participating learners. Our solution organizeslearners in a chain and encrypts all traffic to reduce the controller of the aggregation to a mere messagebroker. 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 andwithout failover, respectively.

Federated learning multi-party computation

THOMAS SANDHOLM、SAYANDEV MUKHERJEE、BERNARDO HUBERMAN

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NextGen Systems, CableLabs, Santa Clara, United States

2025

ACM Transactions on Modeling and Performance Evaluation of Computing Systems
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