  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.
