Page 26 - IIMV Newsletter_30 May 2024

PLACEMENTS

Prof. Deepika Gupta, Chairperson - Career
Development Services & Alumni Relations, IIMV
can be reached at cdschair@iimv.ac.in , and Mr.
Somashekara M.N, in Charge - Career Development

Services & Alumni Relations, IIMV, can be reached at
cds@iimv.ac.in, somashekara.mn@iimv.ac.in.

RESEARCH @ IIMV

Faculty Research Publications

Towards cross-silo federated learning for corporate
organizations

Saikishore Kalloori., Abhishek Srivastava

Published in Knowledge-Based Systems (ABDC-A)

Digital media companies rely on machine learning models to target
their content toward their audience’s interests. Machine learning
models usually rely on the amount and quality of training data. While
today, data is abundant, it is typically stored in data silos and cannot
be shared between companies or publishers due to data protection
and user privacy. Federated Learning (FL) is a distributed machine

learning approach that is rapidly gaining popularity and enables
collaboratively training machine learning models on a large corpus
of decentralized data. Prior research on FL mainly focuses on an FL
setup containing millions of clients. For example, a client may be a
single user’s mobile device with data. However, we note that, in many
scenarios, corporate organizations such as news media companies
that have available data from multiple sets of users could also benefit
from FL. In this work, we aim to focus on building FL models where
multiple corporate organizations like news media companies or

banks participate in the training process of FL to collaboratively train
federated models. We used federated learning to train models for a set
of corporate stakeholders and applied FL for two tasks: a classification
task and a ranking task. For the classification task, we designed a

23 VOL.5/ ISSUE 2, JAN-APRIL 2024