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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: May 2026
Volume 11 | Issue 5
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 120
Article Submitted : 24205
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Total Authors : 24597
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| Paper Title: | Federated LSTM Framework for Privacy-Preserving Decentralized Sales Forecasting Across Distributed Retail Environments with Secure Learning |
| Authors Name: | Om Anand , Shreyash Singh , Yash Kirt Dwivedi , C Geetha |
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IJRTI_211241
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| Published Paper Id: | IJRTI2604213 |
| Published In: | Volume 11 Issue 4, April-2026 |
| DOI: | |
| Abstract: | The issue that this paper is dealing with is the problem of decentralized retailing demand forecasting under strict data privacy limitations and extremely non-homogeneous data distribution across outlets. Existing centralized deep learning schemes do not work because of the limitation of governance and non-IID data. In a bid to address this, we suggest a proposal of an Adaptive Federated Hybrid Intelligence Framework which combines Adaptive Federated Proximal Optimization and an Attention-Guided Multi- Scale Temporal Convolutional Bidirectional Long Short- Term Memory Network. The framework proposes hyperminded volume aggregation to change client inputs dynamically and implements individualization within every round of training. The strategies for the optimization of lightweight parameters enhance communication efficiency. When evaluated in the real- world retail datasets, it has been shown that it has a higher convergence stability, lower-communication overhead, and much higher forecasting accuracy than both Transformer-based models and centralized models. The suggested system serves as a good way to increase robustness and scalability of the decentralized retail analytics environment. |
| Keywords: | Federated Learning, Retail Demand Forecasting, Non-IID Data, Temporal Deep Learning, Adaptive Aggregation, Personalized Models, Time Series Prediction. |
| Cite Article: | "Federated LSTM Framework for Privacy-Preserving Decentralized Sales Forecasting Across Distributed Retail Environments with Secure Learning", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.b574-b579, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604213.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2604213
Registration ID:211241
Published In: Volume 11 Issue 4, April-2026
DOI (Digital Object Identifier):
Page No: b574-b579 Country: Chennai, Tamil Nadu, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2604213 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2604213 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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