Efficient Node Selection in Private Personalized Decentralized Learning
Northern Lights Deep Learning Workshop(2023)
摘要
Personalized decentralized learning is a promising paradigm for distributed
learning, enabling each node to train a local model on its own data and
collaborate with other nodes to improve without sharing any data. However, this
approach poses significant privacy risks, as nodes may inadvertently disclose
sensitive information about their data or preferences through their
collaboration choices. In this paper, we propose Private Personalized
Decentralized Learning (PPDL), a novel approach that combines secure
aggregation and correlated adversarial multi-armed bandit optimization to
protect node privacy while facilitating efficient node selection. By leveraging
dependencies between different arms, represented by potential collaborators, we
demonstrate that PPDL can effectively identify suitable collaborators solely
based on aggregated models. Additionally, we show that PPDL surpasses previous
non-private methods in model performance on standard benchmarks under label and
covariate shift scenarios.
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关键词
selection,learning
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