Decentralized learning (DL) offers a novel paradigm in machine learning by
distributing training across clients without central aggregation, enhancing
scalability and efficiency. However, DL's peer-to-peer model raises challenges
in protecting against inference attacks and privacy leaks. By forgoing central
bottlenecks, DL demands privacy-preserving aggregation methods to protect data
from 'honest but curious' clients and adversaries, maintaining network-wide
privacy. Privacy-preserving DL faces the additional hurdle of client dropout,
clients not submitting updates due to connectivity problems or unavailability,
further complicating aggregation.
This work proposes three secret sharing-based dropout resilience approaches
for privacy-preserving DL. Our study evaluates the efficiency, performance, and
accuracy of these protocols through experiments on datasets such as MNIST,
Fashion-MNIST, SVHN, and CIFAR-10. We compare our protocols with traditional
secret-sharing solutions across scenarios, including those with up to 1000
clients. Evaluations show that our protocols significantly outperform
conventional methods, especially in scenarios with up to 30% of clients dropout
and model sizes of up to $10^6$ parameters. Our approaches demonstrate markedly
high efficiency with larger models, higher dropout rates, and extensive client
networks, highlighting their effectiveness in enhancing decentralized learning
systems' privacy and dropout robustness.

分布式学习是机器学习中一种新颖的范例，通过在客户端之间分发训练数据而不进行集中聚合来提升可扩展性和效率。本文提出了三种基于秘密共享的抗丢失模型的隐私保护方法，通过在多个数据集上进行实验评估了这些方法的效率、性能和准确性。实验结果表明，我们的方法在保护隐私和抵御客户端丢失的场景中表现显著优于传统方法，尤其适用于大型模型、较高的丢失率和复杂的客户端网络。