Using Intel SGX to improve private neural network training and inference

Karl, Ryan; Takeshita, Jonathan; Jung, Taeho

doi:10.1145/3384217.3386399

Cited by 3 publications

(2 citation statements)

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“…This has given rise to Federated Learning (FL) frameworks which aim at preserving data privacy. Another reason for training ML models in a distributed manner is due to massive computations of their growing scale 2 [1]- [2], hence careful allocation of processing ML models on distributed and networked computers plays a crucial role in significantly reducing the execution time 3 .…”

Section: Introductionmentioning

confidence: 99%

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Bottleneck Time Minimization for Distributed Iterative Processes: Speeding Up Gossip-Based Federated Learning on Networked Computers

Kiamari,

Krishnamachari

2021

Preprint

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We present a novel task scheduling scheme for accelerating computational applications involving distributed iterative processes that are executed on networked computing resources. Such an application consists of multiple tasks, each of which outputs data at each iteration to be processed by neighboring tasks; these dependencies between the tasks can be represented as a directed graph. We first mathematically formulate the problem as a Binary Quadratic Program (BQP), accounting for both computation and communication costs. We show that the problem is NP-hard. We then relax the problem as a Semi-Definite Program (SDP) and utilize a randomized rounding technique based on sampling from a suitably-formulated multi-variate Gaussian distribution. Furthermore, we derive the expected value of bottleneck time. Finally, we apply our proposed scheme on gossip-based federated learning as an application of iterative processes. Through numerical evaluations on the MNIST and CIFAR-10 datasets, we show that our proposed approach outperforms well-known scheduling techniques from distributed computing. In particular, for arbitrary settings, we show that it reduces bottleneck time by 91% compared to HEFT and 84% compared to throughput HEFT.

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Section: Introductionmentioning

confidence: 99%

“…3. High-performance ML training with privacy guarantees can be achieved on trusted distributed computing platforms by utilizing recently developed techniques [3].…”

Section: Introductionmentioning

confidence: 99%