  In the resource-constrained IoT-edge environment, Split Federated (SplitFed)
learning is implemented to enhance training efficiency. This method involves
each IoT device dividing its full DNN model at a designated layer into a
device-side model and a server-side model, then offloading the latter to the
edge server. However, existing research overlooks four critical issues as
follows: (1) the heterogeneity of IoT devices' resource capacities and the
sizes of their local data samples impact training efficiency; (2) the influence
of the edge server's computation and network resource allocation on training
efficiency; (3) the data leakage risk associated with the offloaded server-side
sub-model; (4) the privacy drawbacks of current centralized algorithms.
Consequently, proactively identifying the optimal cut layer and server resource
requirements for each IoT device to minimize training latency while adhering to
data leakage risk rate constraint remains a challenging issue. To address these
problems, this paper first formulates the latency and data leakage risk of
training DNN models using Split Federated learning. Next, we frame the Split
Federated learning problem as a mixed-integer nonlinear programming challenge.
To tackle this, we propose a decentralized Proactive Model Offloading and
Resource Allocation (DP-MORA) scheme, empowering each IoT device to determine
its cut layer and resource requirements based on its local multidimensional
training configuration, without knowledge of other devices' configurations.
Extensive experiments on two real-world datasets demonstrate that the DP-MORA
scheme effectively reduces DNN model training latency, enhances training
efficiency, and complies with data leakage risk constraints compared to several
baseline algorithms across various experimental settings.
