International Journal of Leading Research Publication

E-ISSN: 2582-8010   •   Impact Factor: 9.56

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Monthly Scholarly International Journal

Call for Paper Volume 7 Issue 1 January 2026 Submit your research before last 3 days of to publish your research paper in the issue of January.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 7 Issue 1
January 2026

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

Designing Federated Learning Systems for Collaborative Financial Analytics

Author(s) Balaji Soundararajan
Country United States
Abstract Federated Learning (FL) has emerged as a transformative paradigm for privacy-preserving collaborative machine learning, particularly in the financial sector, where data privacy and regulatory compliance are paramount. By enabling decentralized model training across distributed datasets without centralized data aggregation, FL addresses critical challenges in financial analytics, such as fraud detection, risk assessment, credit scoring, and cross-institutional insights. We will explore the principles, applications, and challenges of FL in finance, emphasizing its potential to enhance model robustness, ensure data sovereignty, and comply with stringent regulations like GDPR and anti-money laundering frameworks. Key challenges include data heterogeneity, secure aggregation techniques, regulatory alignment, and resistance to adversarial attacks. Case studies from banking, regulatory bodies, and financial intermediaries illustrate successful implementations, underscoring FL’s capacity to unlock collaborative insights while preserving confidentiality. The study concludes with design principles for scalable, secure FL systems and highlights future directions for adoption in global financial ecosystems.
Keywords Federated Learning, Financial Analytics, Privacy-Preserving Machine Learning, Secure Aggregation, Regulatory Compliance, Data Heterogeneity, Decentralized Model Training, Blockchain, Fraud Detection, Case Studies
Field Engineering
Published In Volume 5, Issue 2, February 2024
Published On 2024-02-06
DOI https://doi.org/10.5281/zenodo.15051139
Short DOI https://doi.org/g88zzd
View / Download PDF File

Share this



CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJLRP DOI prefix is
10.70528/IJLRP

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 6 Isu 12 Cover Page Vol 6 Isu 10 Cover Page Vol 6 Isu 9

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJLRP
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9687-828-838 editor@ijlrp.com