# Rec-AD: An Efficient Computation Framework for FDIA Detection Based on Tensor Train Decomposition and Deep Learning Recommendation Model

arXiv: https://arxiv.org/abs/2507.14668 · HTML: https://mlsanity.com/paper/2507.14668/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 49 references that are in the map.
- First posted on arXiv 19 Jul 2025.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Learning Recommendation Model for Personalization and Recommendation Systems](https://mlsanity.com/paper/1906.00091/) (2019)
- [Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models](https://mlsanity.com/paper/2104.05158/) (2021)
- [Distributed Hierarchical GPU Parameter Server for Massive Scale Deep Learning Ads Systems](https://mlsanity.com/paper/2003.05622/) (2020)
- [TT-Rec: Tensor Train Compression for Deep Learning Recommendation Models](https://mlsanity.com/paper/2101.11714/) (2021)
- [DeepRecSys: A System for Optimizing End-To-End At-scale Neural Recommendation Inference](https://mlsanity.com/paper/2001.02772/) (2020)
- [Learnable Embedding Sizes for Recommender Systems](https://mlsanity.com/paper/2101.07577/) (2021)
- [Understanding Capacity-Driven Scale-Out Neural Recommendation Inference](https://mlsanity.com/paper/2011.02084/) (2020)
- [Compressing Recurrent Neural Network with Tensor Train](https://mlsanity.com/paper/1705.08052/) (2017)
- [Post-Training 4-bit Quantization on Embedding Tables](https://mlsanity.com/paper/1911.02079/) (2019)
- [Accelerating Recommendation System Training by Leveraging Popular Choices](https://mlsanity.com/paper/2103.00686/) (2021)
- [Centaur: A Chiplet-based, Hybrid Sparse-Dense Accelerator for Personalized Recommendations](https://mlsanity.com/paper/2005.05968/) (2020)
- [Parallax: Sparsity-aware Data Parallel Training of Deep Neural Networks](https://mlsanity.com/paper/1808.02621/) (2018)
- [Mixed-Precision Embedding Using a Cache](https://mlsanity.com/paper/2010.11305/) (2020)
- [ScaleFreeCTR: MixCache-based Distributed Training System for CTR Models with Huge Embedding Table](https://mlsanity.com/paper/2104.08542/) (2021)

## Built on by (most cited first, 1 of 1)

- [Clustered Federated Learning for Generalizable FDIA Detection in Smart Grids with Heterogeneous Data](https://mlsanity.com/paper/2507.14999/) (2025)

## Abstract (arXiv)

Deep learning models have been widely adopted for False Data Injection Attack (FDIA) detection in smart grids due to their ability to capture unstructured and sparse features. However, the increasing system scale and data dimensionality introduce significant computational and memory burdens, particularly in large-scale industrial datasets, limiting detection efficiency. To address these issues, this paper proposes Rec-AD, a computationally efficient framework that integrates Tensor Train decomposition with the Deep Learning Recommendation Model (DLRM). Rec-AD enhances training and inference efficiency through embedding compression, optimized data access via index reordering, and a pipeline training mechanism that reduces memory communication overhead. Fully compatible with PyTorch, Rec-AD can be integrated into existing FDIA detection systems without code modifications. Experimental results show that Rec-AD significantly improves computational throughput and real-time detection performance, narrowing the attack window and increasing attacker cost. These advancements strengthen edge computing capabilities and scalability, providing robust technical support for smart grid security.