Next‑Generation Predictive Modeling with Machine Learning:

Advancing Cross‑Industry Intelligence through Federated, Adaptive

and Interpretable Systems

S. Prabagar

, Deepika Pradeep Patil

, S. Rajeswari

, M. Jeevaa

, R. Vishalakshi

and Akilan S.

Department of Computer Science and Engineering, COE in IoT, Alliance School of Advanced Computing, Alliance

University, Karnataka, India

Department of Electronics and Computer Science Engineering, Shah and Anchor Kutchhi Engineering College, Chembur

East, Mumbai, Maharashtra, India

Department of Information Technology, J.J. College of Engineering and Technology, Tiruchirappalli, Tamil Nadu, India

School of Computing and Information Technology, REVA University, Bangalore, India

Department of Computer Science and Engineering (Data Science), Vardhaman College of Engineering, Shamshabad,

Hyderabad, Telangana, India

Department of MCA, New Prince Shri Bhavani College of Engineering and Technology, Chennai, Tamil Nadu, India

Keywords: Machine Learning, Predictive Modeling, Federated Learning, Cross‑Industry Applications, Model

Interpretability.

Abstract: The rise of machine learning has rapidly changed predictive modeling in any industrial sector, where systems

have transformed from being data-driven to adaptive, safe and interpretable. The present work investigates,

how the benefits of emerging machine learning frameworks such as federated learning, ensemble strategies,

and transfer learning – can be combined to address limitations that exist with regards to scalability, bias, and

real-time capabilities. By reviewing healthcare diagnosis, financial fraud detection, environmental prediction

and industry 4.0 applications, the study shows how our new class of ML algorithms can offer both

explainability and actionable results, and at the same time, offer data privacy and resistance to adversarial

attacks. The proposed structure highlights its adjustable nature on volatile datasets, transparency in decision-

making, and applicability to various industries. This paper anchors machine learning as not only predictive,

but as a strategic enabler of intelligent automation at scale in all industries.

1 INTRODUCTION

Data is growing at an exponential rate across all

industries and predictive modelling is now at the heart

of making intelligent decisions with machine

learning being the primary technology powering this

change. In fields such as healthcare, finance,

manufacturing, and environmental monitoring,

machine learning is being more and more utilized to

predict results, identify anomalies, and improve

processes. In contrast to statistical models, machine

learning provides great flexibility and the ability to

identify intricate patterns from large high

dimensional data. Furthermore, recent techniques

such as federated learning enable modelling with

decentralized data without breaching individual

privacy and models based on ensemble and deep

learning approaches improve predictive accuracy in

challenging settings. Also, the development of

explainable and interpretable AI frameworks acts as

a response to the need for transparency and trust in

automated systems, which triggered the need for

explainability and interpretability in the AI field.

These advances are re-defining the possibilities of

predictive modelling by enabling more than just a

forecast, but also by enabling intelligence that is

resilient, ethical, and domain-adaptive.

In this paper we analyse how advances in next-

generation machine learning frameworks are not only

increasing accuracy but also enabling cross-industry

use cases by tackling underlying issues such as data

heterogeneity, security, and scalability. By offering a

consolidated view of these developments, the work

Prabagar, S., Patil, D. P., Rajeswari, S., Jeevaa, M., Vishalakshi, R. and S., A.

Next-Generation Predictive Modeling with Machine Learning: Advancing Cross-Industry Intelligence through Federated, Adaptive and Interpretable Systems.

DOI: 10.5220/0013944400004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 5, pages

827-832

ISBN: 978-989-758-777-1

827

strategically locates machine learning as a

fundamental enabler for intelligent, context-aware

automation in today's industry.

2 PROBLEM STATEMENT

Even though industries now use predictive models

underpinned by machine learning (ML) routinely,

numerous problems stifle the full benefits of these

technologies for many industries. A traditional model

is built on centralized datasets which are susceptible

to privacy leak and are not compatible with

decentralized, fragmented real-world data. Moreover,

concerns about model interpretability (debriefing),

performance transfer across domains, and fairness of

decision-making challenge the ethical and practical

utility of predictive models. Those constraints stand

out in high-stakes realms like health care, finance,

and manufacturing, where diversity and complexity

of data call for more than merely being right they

demand recourse, openness, and resilience.

Solutions are either focused on domain-dependent

uses or are not capable of bringing state-of-the-art

advances, such as federated learning, transfer

learning and explainable AI together in a manner

suitable for cross-industry exploitation. A next

generation predictive modelling methodology that

integrates these state-of-the-art techniques for

handling actual situations is urgently required. This

research attempts to address such a gap by creating a

machine-learning based framework, which can

provide trustworthy and scalable predictions for

various industrial contexts.

3 LITERATURE SURVEY

Predictive modeling is another field where machine

learning techniques have been extensively used, as it

offers the ability to discover intricate patterns hidden

in the data and to aid in high-accuracy decision

making in many domains. In health area, models

similar to the one analyzed by Pfohl et aL (6) have

also been introduced. (2021) and Dayan et al. (2021)

demonstrate the potential of ML in improving clinical

risk prediction and patient outcome forecasting, but

there are limitations such as fairness in data and

privacy, that continue to be the focus of concern and

need to be addressed. To mitigate these, federated

learning was brought forth, allowing the distributed

training of models without accessing sensitive patient

information (Rieke et al., 2020; Guo et al., 2021).

Islam et al. (2021) demonstrated how deep

learning played a critical role in COVID-19

diagnosis, where ML made its presence felt in times

of crisis. Likewise, in public health, Olawade et al.

(2023) focused on getting it right, at scale, with the

aid of artificial intelligence. Putra et al. (2021) used

ML for environmental monitoring, and they used the

concept of edge computing to predict the PM2. 5

levels with good data integrity and responsiveness.

In biological imaging, ensemble models and CNN

structures were drastically improved diagnostic

accuracy in the field of diagnosis. Valenkova 2025

and Manna et al. (2021) proposed CNN for MRI

segmentation and cytology classification task and

used the fuzzy logic to improve the robustness. Rajput

(2024) and Sundaresan (2021) further improved upon

this work by leveraging triplanar and ensemble U-

Net modalities showing successful segmentation in

brain imaging.

They have also been of benefit to the financial

sector adopting ML based techniques for fraud

detection, wherein Kim and Sohn (2012) suggest peer

group analysis, and Louzada and Ara (2012) b

propose bagging of probabilistic networks. FPLS

Sundarkumar and Ravi (2015) presented the hybrid

undersampling for imbalanced financial datasets

since the imbalance is higher in financial data sets

available in various fraud and risk detection

problems.

Gu et al. (2015) applied ensemble classifiers to

GPCR classification, showing the promise of ML in

bioinformatics. Xue et al. (2020) used transfer

learning for the classification of histopathology

images and reported better generalization in clinical

applications.

Banda et al. (2019) detected undiagnosed familial

hypercholesterolemia cases with ML models from

EHRs, and Lu et al. (2022) and Li et al. (2022)

discussed auditing ML models for fairness and

infusion of AI into collaborative clinical pathways.

The above studies indicate an increasing awareness of

the need for explainability and audability in

predictive computational health systems.

Jung et al. (2016) illustrated that ML is capable of

predicting slow healing in wounds, which will allow

for timely treatment. Related work in which model

interpretability and dimensionality reduction are

concerned, Karray et al. (2021) introduced: A holistic

framework for reducing data complexity while

maintaining interpretability and predictive ability a

prerequisite for high-dimensional data.

In industrial and environmental fields, Chen et al.

(2022) on multimodal fusion between image and

sensor data for better healthcare results and Zhou et

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COMMUNICATION, AND COMPUTING TECHNOLOGIES

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al. (2021) implemented real-time anomaly detection

in the smart factory to cope with respecting speed

and reliability requirements in industrial predictive

systems.

Overall, these studies collectively show a

tendency towards systems with increasing synergy

between ML applications and support for inter-

domain generalization and interpretation. They also

serve as a reminder of the need for models that are

not only effective but interpretable, robust, and

adaptable in the open world. This inventory serves as

basis to further develop a unified framework to

exploit these breakthroughs for cross-industry

predictive modelling.

4 METHODOLOGY

The study leverages a modular and adaptive ML

methodology that unifies the federated learning,

ensemble modelling, explainable AI and is applicable

for predictive modelling in multiple sectors. The

method is intended for a wide range of data sources,

different data amounts, and privacy preserving

computation. Raw Material The data acquisition

process is initiated when structured and unstructured

data from various sources such as health care,

finance, environmental monitoring, and smart

manufacturing systems are gathered (Fig. 6.1). These

datasets are processed to normalize, remove outliers,

as well as impute missing values, and retain

underlying patterns to be used in training.

To overcome the issues of data privacy and

decentralized learning, the approach leverages

federated learning protocols that enable the nodes, or

clients to train local models without sharing raw data.

Updates at global level are aggregated by a central

server which keeps global model. This configuration

is especially advantageous for applications in

regulated industries like healthcare and finance.

Figure 1 show the Training Convergence Trend in

Federated Learning Across Nodes.

Figure 1: Training Convergence Trend in Federated

Learning Across Nodes.

Table 1: Preprocessing techniques applied to the collected

datasets.

Preprocessi

ng Step

Technique

Use

Purpose

Missing

Data

Handling

Mean/Mode

Imputation

Fill gaps without

biasing trends

Normalizati

Min-Max

Scaling

Uniform value

range for all

features

Categorical

Encodin

One-Hot

Encodin

Convert text to

machine-readable

Feature

Selection

Recursive

Elimination

Remove irrelevant

or nois

features

Data

Balancing

SMOTE

Improve model

fairness and recall

To better improve the prediction results and stability,

ensemble learning algorithms including stacking,

bagging and boosting are introduced. These models

are adapted to the idiosyncrasies of the domains, but

also retain generality. Finally, the ensemble

predictions are interpreted using transparent models

such as SHAP (SHapley Additive exPlanations) and

LIME (Local Interpretable Model-agnostic

Explanations) and insights are produced that are

meaningful for stakeholders in their respective

domains. Table 1 show the Pre-processing

Techniques Applied to the Collected Datasets. To

maintain evaluation consistency the models are

evaluated by cross-validation and domain specific

metrics such as accuracy, precision, recall, F1-score,

AUC-ROC and domain defined cost-based metrics.

Every model is submitted to the controlled

environment of the simulated real-time industry

dataflow for latency, scalability and fault tolerance

testing. Figure 2 show the Cross-Industry Machine

Learning Framework for Predictive Modelling.

Figure 2: Cross-industry machine learning framework for

predictive modeling.

Next-Generation Predictive Modeling with Machine Learning: Advancing Cross-Industry Intelligence through Federated, Adaptive and

Interpretable Systems

829

The last framework is the iterative learning that

evolves from input feedback of each simulation

(cyclic model optimization strategy). The outcomes

are a multi-disciplinary machine learning framework

that can be easily configured to different industry

demands with consideration of privacy, explainability

and scalability. This approach not only provides a

practical guidance of embedding state-of-the-art ML

principles into industrial practice, but also serves a

benchmarking model for the prospective predictive

schemes. Table 2 show the Effect of Hyperparameter

Optimization on Model Accuracy.

Table 2: Effect of hyperparameter optimization on model

accuracy.

Model

Defau

Accur

Tune

Accur

Improv

ement

Optimizati

Algorithm

Use

XGB

oos

0.84 0.90 +7.1%

Grid

Feder

ated

CNN

0.86 0.91 +5.8%

Random

Rand

Fores

0.82 0.88 +7.3%

Bayesian

Optimizati

Tripl

anar

U-

0.89 0.94 +5.6%

Manual

Tuning

5 RESULT AND DISCUSSION

The application of the generated machine learning

architecture proved to be successful in a number of

industrial case studies, confirming its versatility,

accuracy, and scalability. In medicine, the federated

learning model was able to preserve patient privacy

and achieved competitive diagnostic performance

with centralized models. Table 3 show the

Comparative Analysis of Predictive Model

Performance Metrics For example, the distributed

hospital dataset-based vigilance early detection

model obtained an average 0.91 F1-score, thus

indicating good discriminative power without

directly sharing the data. The interpretability tier also

offered transparent rationales grounded in the clinical

data for each prediction, which prompted increased

confidence in AI-integrated decisions among

clinicians.

Table 3: Comparative analysis of predictive model

performance metrics.

Model

Name

Acc

urac

Prec

ision

cal

F1-

Scor

AUC-

ROC

Federated

CNN

0.91 0.89

0.91 0.95

Random

Fores

0.88 0.86

0.86 0.91

XGBoost 0.90 0.88

0.88 0.93

Triplanar

U-

0.94 0.92

0.93 0.96

LSTM

(Time

Series)

0.87 0.85

0.85 0.90

The ensemble learning method increased the ability

to testify rare fraud patterns in financial fraud

detection, particularly when working with strongly

imbalanced datasets. Hybrid under sampling and

boosting algorithms not only improved recall but

decreased false positives, which is important in

operational risk mitigation applications. These

findings indicate the model's ability to capture high-

risk, low-frequency events that are common in

financial systems. Figure 3 show the Comparative

Performance of Predictive Models.

Figure 3: Comparative performance of predictive models.

ICRDICCT‘25 2025 - INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION,

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Table 4: Adaptability and performance retention across

industrial domains.

Source

Domain

Target

Domai

Adaptation

Technique

Accura

Retain

Model

Reusabi

lity

Healthc

are

Financ

Transfer

Learning +

Fine-

Tuning

92% High

Manufa

cturing

Enviro

nment

Domain

Adaptation

ers

88% Mediu

Environ

ment

Health

care

Normalizati

on + Re-

wei

htin

90% High

In the context of environment monitoring, edge-

learning predictive models used real-time sensor

measurements in order to predict PM2. 5 That's that

the lowest latency, 5 monthlies with the best. The

proposed light weight ML models facilitated high rate

processing and preserved the accuracy which indicate

the system’s potential to be employed within the

context of smart cities and IOT based infrastructure.

It is worth noting that the adaptability of the model to

perform efficiently under low-resource conditions

demonstrates its importance on scalable

environmental intelligence. Table 4 show the

Adaptability and Performance Retention Across

Industrial Domains.

Figure 4: Shap-based feature importance across domains.

In smart manufacturing, the real-time anomaly

detection module was shown to be successful in

detecting the deviations in the operating behaviour

well before the system failures take place. Integrating

temporal modelling with explainable outputs might

allow maintenance teams to rank interventions

according to not only the predicted risk, but also on

the background of each alert. This led to less

downtime and more efficient allocation of resources.

Figure 4 show the SHAP-based Feature Importance

Across Domains.

In the entire spectrum, the explainability factors

like SHAP and LIME helped to boost the user's

confidence demystifying some of the model's black

boxes decisions. Moreover, the federated structure

catered well with sensitivity of data particularly in

industry verticals which are highly regulated in terms

of compliance. Our results emphasize the need to

adopt a holistic viewpoint where privacy,

performance, and interpretability are all treated

equally seriously when attempting to engineer

reliable predictive systems. The strong results on

these diverse benchmark tasks prove the

generalisability of the framework and indicates that it

can be a potential base model for the next generation

of intelligent systems for automation. Figure 5 show

the Latency Comparison for Real-Time Deployment

Figure 5: Latency comparison for real-time deployment.

6 CONCLUSIONS

In this research, a robust and adaptive machine

learning framework is provided to the challenges of

predictive modelling in various applications. The

proposed framework managed to address some

important challenges such as data privacy,

interpretability and cross-domain generalization

using the combination of federated learning,

ensemble methods and explainable AI. These results

in healthcare, finance, environmental monitoring and

smart manufacturing show that predictive models can

remain accurate while respecting operational

boundary conditions and ethical considerations.

Its goal-trained approach to knowledge

generation, who is trained directly on end-goals rather

than to the agent, and capacity to reason about

decentralized data sources in a provably secure but

Next-Generation Predictive Modeling with Machine Learning: Advancing Cross-Industry Intelligence through Federated, Adaptive and

Interpretable Systems

831

privacy-preserving manner, establish a new bar for

responsible AI deployment. It not only improves

predictability, but also increases the end-users and

stakeholders' trust and transparency. With successful

experimental evaluation, the model opens up avenues

for scalable, robust and intelligent automation

systems which can dynamically adjust themselves to

complex behaviour of any given industry. This work,

therefore, represents a major stepping stone towards

next-gen machine learning-based predictive systems.

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