Author Guidelines & Templates

Follow these guidelines and use the templates below to ensure your submission meets NEXARA's standards.

Sample Paper (PDF)

View template below

Declaration Form

View template below

Manuscript Formatting Guidelines

Paper Size	A4 (210 × 297 mm)
Margins	1 inch (2.54 cm) on all sides
Font	Times New Roman, 12pt (body); 14pt bold (title)
Line Spacing	1.5 for body text; single for references
Title	Centered, 14pt bold, Title Case
Abstract	150–300 words, single paragraph, italic
Keywords	4–8 keywords, comma separated
Headings	Bold, numbered (1. Introduction, 2. Literature Review…)
Figures & Tables	Numbered, captioned, placed near first mention
References	APA 7th Edition format
File Format	.docx or .pdf (max 5 MB)
Plagiarism Limit	Below 15% (checked via iThenticate/Turnitin)
Page Limit	5–25 pages (including references)

Sample Paper Template

Below is the required paper structure. Follow this format exactly for fast acceptance.

NEXARA — International Journal of Emerging Research & Innovation

E-ISSN: Applied (Pending Assignment) | DOI: 10.5281/zenodo.XXXXXX

A Novel Framework for Federated Learning in Edge Computing Environments: Performance Optimization and Privacy Preservation

Dr. Ananya Sharma^1*, Prof. Rajesh Kumar², Dr. Meera Nair³

¹Department of Computer Science, IIT Delhi, New Delhi, India
²School of AI & Data Science, IIIT Hyderabad, India
³Faculty of Computing, National University of Singapore, Singapore

*Corresponding author: ananya.sharma@iitd.ac.in

Abstract

This paper proposes a novel federated learning framework optimized for resource-constrained edge computing environments. The framework introduces an adaptive model aggregation strategy that reduces communication overhead by 47% while maintaining model accuracy within 2% of centralized training baselines. We evaluate our approach across three real-world datasets (healthcare IoT, autonomous vehicles, and smart manufacturing) and demonstrate significant improvements in convergence speed, privacy preservation, and computational efficiency. Our differential privacy mechanism achieves ε=0.3 privacy guarantees without substantial utility loss. The results suggest that the proposed framework enables practical deployment of privacy-preserving machine learning in heterogeneous edge environments with limited bandwidth and computational resources.

Keywords: Federated Learning, Edge Computing, Privacy Preservation, Model Aggregation, Differential Privacy, IoT, Distributed Machine Learning

1. Introduction

[Introduce the research problem, its significance, and research gaps. State your research objectives and contributions clearly. Include 2–3 paragraphs covering: background context, problem statement, and paper organization.]

The rapid proliferation of Internet of Things (IoT) devices has generated unprecedented volumes of data at the network edge. Traditional centralized machine learning approaches face critical challenges including bandwidth limitations, latency constraints, and growing privacy concerns (Smith et al., 2023). Federated learning has emerged as a promising paradigm...

2. Literature Review

[Comprehensive review of existing research. Cite at least 15–20 relevant works. Identify gaps your research addresses. Use APA 7th edition in-text citations.]

McMahan et al. (2017) introduced the foundational FedAvg algorithm for federated learning, demonstrating feasibility across non-IID data distributions. Subsequent work by Li et al. (2020) addressed convergence challenges through FedProx...

3. Methodology

[Describe your research methodology in detail. Include: research design, data collection methods, sample size, tools used, analytical framework, and any mathematical formulations.]

4. Results and Discussion

[Present findings with tables, figures, and statistical analysis. Discuss implications, compare with existing literature, and explain anomalies.]

Table 1: Performance Comparison Across Datasets

Method	Accuracy (%)	Comm. Rounds	Privacy (ε)
FedAvg	87.3	200	∞
FedProx	88.1	180	∞
Proposed	89.6	106	0.3

5. Conclusion

[Summarize key findings, state contributions, acknowledge limitations, and suggest future research directions. 1–2 paragraphs.]

References

Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., & Smith, V. (2020). Federated optimization in heterogeneous networks. Proceedings of MLSys, 2, 429–450.

McMahan, B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. Proceedings of AISTATS, 1273–1282.

Smith, J., Chen, W., & Patel, R. (2023). Privacy-preserving machine learning at the edge: A comprehensive survey. IEEE Transactions on Neural Networks and Learning Systems, 34(5), 2341–2360.

[Include 15–20 references in APA 7th Edition format]

Copyright Transfer Agreement

All accepted papers require a signed copyright transfer agreement. Below is the standard form — fill, sign, and submit with your manuscript.

NEXARA — Copyright Transfer Agreement

International Journal of Emerging Research & Innovation

E-ISSN: Applied (Pending Assignment) | DOI: 10.5281/zenodo.XXXXXX

Manuscript Title:

________________________________________

Paper ID / Submission ID:

NXR-________-________

Author(s) Full Name(s):

________________________________________

Affiliation / Institution:

________________________________________

Terms of Copyright Transfer

By signing this agreement, the Author(s) hereby transfer to NEXARA Publications the copyright of the above-identified manuscript, subject to the following terms:

License Grant: The work will be published under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Authors retain the right to share, reuse, and build upon the work with proper attribution.
Open Access: The published article will be freely accessible to the public without any subscription or paywall.
Author Rights Retained: Authors retain the right to: (a) use the article for personal, educational, or institutional purposes; (b) include the article in thesis or dissertations; (c) post preprints on personal websites or institutional repositories; (d) present the work at conferences.
Originality Warranty: The Author(s) warrant that the manuscript is original, unpublished, and not under consideration elsewhere. The Author(s) further warrant that the work does not infringe upon any copyright, proprietary right, or personal right of any third party.
Third-Party Content: If the manuscript includes content (figures, tables, data) from third-party sources, the Author(s) confirm that appropriate permissions have been obtained and credited.
Corresponding Author Authority: The corresponding author signs on behalf of all co-authors and warrants that all co-authors have reviewed and approved the final version of the manuscript and this agreement.
Retraction: NEXARA reserves the right to retract the article if evidence of plagiarism, data fabrication, or ethical violations is discovered post-publication.

Corresponding Author Signature:

Date:

Print Name:

Email:

This form must be signed by the corresponding author on behalf of all co-authors. Digital signatures are accepted. Submit this form along with your manuscript via the submission portal.

Author Declaration Form

Every submission must include a signed declaration. Use the template below.

Author Declaration & Undertaking

NEXARA — International Journal of Emerging Research & Innovation

Manuscript Title:

________________________________________

Author(s):

________________________________________

Submission ID:

NXR-________-________

I/We, the undersigned author(s), hereby declare and undertake the following:

1. The submitted manuscript is original work and has not been previously published (in whole or in part) in any journal, conference proceedings, book, or other publication.

2. The manuscript is not currently under review or consideration for publication at any other journal or publication venue.

3. All data, results, analyses, figures, and tables presented in the manuscript are genuine, accurate, and have not been fabricated, falsified, or manipulated in any way.

4. All sources of information, data, and intellectual contributions from other works have been properly cited and acknowledged in accordance with academic standards (APA 7th Edition).

5. The plagiarism similarity index of this manuscript is below the journal's threshold of 15%, as verified by the author(s) using appropriate plagiarism detection tools.

6. All co-authors listed on the manuscript have made substantial intellectual contributions to the work and have reviewed and approved the final submitted version.

7. No part of this work infringes upon the copyright, intellectual property rights, privacy rights, or any other legal rights of any third party.

8. Where applicable, appropriate ethical clearance / Institutional Review Board (IRB) approval has been obtained for research involving human subjects, animal subjects, or sensitive data.

9. All conflicts of interest (financial, personal, institutional, or otherwise) have been fully disclosed to the editor at the time of submission.

10. I/We understand that violation of any of the above declarations may result in immediate rejection, retraction of the published article, permanent ban from future submissions, and reporting to the author's institution and relevant authorities.

⚠️ Identity Verification (Effective April 2026)

As per NEXARA's updated policy, all submitting and co-authors must provide government-issued photo ID (passport, Aadhaar, national ID, or driver's license) and institutional email verification. ORCID iD is strongly recommended. Submitted documents are encrypted and used solely for verification (retained for 90 days post-decision).

Signature:

Print Name:

Date:

Institution:

Email:

Submission Checklist

Before submitting, ensure you have the following:

Manuscript in .docx or .pdf format (max 5 MB)

Abstract: 150–300 words with 4–8 keywords

References in APA 7th Edition format

Plagiarism report (below 15% similarity)

Signed Copyright Transfer Agreement

Signed Author Declaration Form

Government-issued ID (from April 2026)

Institutional email verification

ORCID iD (strongly recommended)

All co-authors listed with affiliations

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