  Federated Learning (FL) is the most widely adopted collaborative learning
approach for training decentralized Machine Learning (ML) models by exchanging
learning between clients without sharing the data and compromising privacy.
However, since great data similarity or homogeneity is taken for granted in all
FL tasks, FL is still not specifically designed for the industrial setting.
Rarely this is the case in industrial data because there are differences in
machine type, firmware version, operational conditions, environmental factors,
and hence, data distribution. Albeit its popularity, it has been observed that
FL performance degrades if the clients have heterogeneous data distributions.
Therefore, we propose a Lightweight Industrial Cohorted FL (LICFL) algorithm
that uses model parameters for cohorting without any additional on-edge
(clientlevel) computations and communications than standard FL and mitigates
the shortcomings from data heterogeneity in industrial applications. Our
approach enhances client-level model performance by allowing them to
collaborate with similar clients and train more specialized or personalized
models. Also, we propose an adaptive aggregation algorithm that extends the
LICFL to Adaptive LICFL (ALICFL) for further improving the global model
performance and speeding up the convergence. Through numerical experiments on
real-time data, we demonstrate the efficacy of the proposed algorithms and
compare the performance with existing approaches.
