Federated Split Learning with Only Positive Labels for
  resource-constrained IoT environment
Praveen Joshi Chandra Thapa Mohammed Hasanuzzaman Ted Scully Haithem Afli
Distributed collaborative machine learning (DCML) is a promising method in
the Internet of Things (IoT) domain for training deep learning models, as data
is distributed across multiple devices. A key advantage of this approach is
that it improves data privacy by removing the necessity for the centralized
aggregation of raw data but also empowers IoT devices with low computational
power. Among various techniques in a DCML framework, federated split learning,
known as splitfed learning (SFL), is the most suitable for efficient training
and testing when devices have limited computational capabilities. Nevertheless,
when resource-constrained IoT devices have only positive labeled data,
multiclass classification deep learning models in SFL fail to converge or
provide suboptimal results. To overcome these challenges, we propose splitfed
learning with positive labels (SFPL). SFPL applies a random shuffling function
to the smashed data received from clients before supplying it to the server for
model training. Additionally, SFPL incorporates the local batch normalization
for the client-side model portion during the inference phase. Our results
demonstrate that SFPL outperforms SFL: (i) by factors of 51.54 and 32.57 for
ResNet-56 and ResNet-32, respectively, with the CIFAR-100 dataset, and (ii) by
factors of 9.23 and 8.52 for ResNet-32 and ResNet-8, respectively, with
CIFAR-10 dataset. Overall, this investigation underscores the efficacy of the
proposed SFPL framework in DCML.