NET-SA: An Efficient Secure Aggregation Architecture Based on In-Network Computing
Qingqing Ren, Wen Wang, Shuyong Zhu, Zhiyuan Wu, Yujun Zhang

TL;DR
NET-SA introduces an in-network computing architecture for privacy-preserving machine learning that significantly reduces communication overhead and improves dropout tolerance through seed homomorphic pseudorandom generators and programmable switches.
Contribution
It presents a novel secure aggregation architecture leveraging in-network computing to minimize communication costs and enhance dropout tolerance in PPML.
Findings
Achieves up to 77x faster runtime compared to existing methods.
Reduces total client communication by 2x.
Demonstrates effectiveness on server clusters and programmable switches.
Abstract
Privacy-preserving machine learning (PPML) enables clients to collaboratively train deep learning models without sharing private datasets, but faces privacy leakage risks due to gradient leakage attacks. Prevailing methods leverage secure aggregation strategies to enhance PPML, where clients leverage masks and secret sharing to further protect gradient data while tolerating participant dropouts. These methods, however, require frequent inter-client communication to negotiate keys and perform secret sharing, leading to substantial communication overhead. To tackle this issue, we propose NET-SA, an efficient secure aggregation architecture for PPML based on in-network computing. NET-SA employs seed homomorphic pseudorandom generators for local gradient masking and utilizes programmable switches for seed aggregation. Accurate and secure gradient aggregation is then performed on the central…
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Taxonomy
TopicsSoftware-Defined Networks and 5G · IoT and Edge/Fog Computing · Energy Efficient Wireless Sensor Networks
