Flaps: Federated learning and privately scaling

sudipta paul, Poushali Sengupta, Subhankar Mishra

Abstract:

Federated learning (FL) is a distributed learning
process where the model (weights and checkpoints) is transferred
to the devices that posses data rather than the classical way
of transferring and aggregating the data centrally. In this way,
sensitive data does not leave the user devices. FL uses the FedAvg
algorithm, which is trained in the iterative model averaging
way, on the non-iid and unbalanced distributed data, without
depending on the data quantity. Some issues with the FL are,
1) no scalability, as the model is iteratively trained over all
the devices, which amplifies with device drops; 2) security and
privacy trade-off of the learning process still not robust enough
and 3) overall communication efficiency and the cost are higher.
To mitigate these challenges we present Federated Learning
and Privately Scaling (FLaPS) architecture, which improves
scalability as well as the security and privacy of the system.
The devices are grouped into clusters which further gives better
privacy scaled turn around time to finish a round of training.
Therefore, even if a device gets dropped in the middle of
training, the whole process can be started again after a definite
amount of time. The data and model both are communicated
using differentially private reports with iterative shuffling which
provides a better privacy-utility trade-off. We evaluated FLaPS
on MNIST, CIFAR10, and TINY-IMAGENET-200 dataset using
various CNN models. Experimental results prove FLaPS to be
an improved, time and privacy-scaled environment having better
and comparable after-learning-parameters with respect to the
central and FL models.