  This paper proposes a novel split learning framework with multiple
end-systems in order to realize privacypreserving deep neural network
computation. In conventional split learning frameworks, deep neural network
computation is separated into multiple computing systems for hiding entire
network architectures. In our proposed framework, multiple computing
end-systems are sharing one centralized server in split learning computation,
where the multiple end-systems are with input and first hidden layers and the
centralized server is with the other hidden layers and output layer. This
framework, which is called as spatio-temporal split learning, is spatially
separated for gathering data from multiple end-systems and also temporally
separated due to the nature of split learning. Our performance evaluation
verifies that our proposed framework shows nearoptimal accuracy while
preserving data privacy.
