Device-Performance-Driven Heterogeneous Multiparty Learning for Arbitrary Images

Yuanqiao Zhang, Maoguo Gong, Yuan Gao, A. K. Qin, Shanfeng Wang

Abstract:

Multiparty learning (MPL) is an emerging framework for privacy-preserving collaborative learning. It enables
individual devices to build a knowledge-shared model and
remaining sensitive data locally. However, with the continuous
increase of users, the heterogeneity gap between data and
equipment becomes wider, which leads to the problem of model
heterogeneous. In this article, we concentrate on two practical
issues: data heterogeneous problem and model heterogeneous
problem, and propose a novel personal MPL method named
device-performance-driven heterogeneous MPL (HMPL). First,
facing the data heterogeneous problem, we focus on the problem
of various devices holding arbitrary data sizes. We introduce
a heterogeneous feature-map integration method to adaptively
unify the various feature maps. Meanwhile, to handle the model
heterogeneous problem, as it is essential to customize models for
adapting to the various computing performances, we propose
a layer-wise model generation and aggregation strategy. The
method can generate customized models based on the device’s
performance. In the aggregation process, the shared model
parameters are updated through the rules that the network
layers with the same semantics are aggregated with each other.
Extensive experiments are conducted on four popular datasets,
and the result demonstrates that our proposed framework
outperforms the state of the art (SOTA).