# Federated Hash Projected Latent Factor Learning

arXiv: https://arxiv.org/abs/2606.26192 · HTML: https://mlsanity.com/paper/2606.26192/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 89 references that are in the map.
- First posted on arXiv 24 Jun 2026.

## Builds on (most cited first, 19 of 19)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Deep Leakage from Gradients](https://mlsanity.com/paper/1906.08935/) (2019)
- [A Survey on Learning to Hash](https://mlsanity.com/paper/1606.00185/) (2016)
- [Secure Federated Matrix Factorization](https://mlsanity.com/paper/1906.05108/) (2019)
- [FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation](https://mlsanity.com/paper/2102.04925/) (2021)
- [Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System](https://mlsanity.com/paper/1901.09888/) (2019)
- [Meta Matrix Factorization for Federated Rating Predictions](https://mlsanity.com/paper/1910.10086/) (2019)
- [Wireless Communications for Collaborative Federated Learning](https://mlsanity.com/paper/2006.02499/) (2020)
- [LightFR: Lightweight Federated Recommendation with Privacy-preserving Matrix Factorization](https://mlsanity.com/paper/2206.11743/) (2022)
- [Dual Personalization on Federated Recommendation](https://mlsanity.com/paper/2301.08143/) (2023)
- [Content-aware Neural Hashing for Cold-start Recommendation](https://mlsanity.com/paper/2006.00617/) (2020)
- [HS-GCN: Hamming Spatial Graph Convolutional Networks for Recommendation](https://mlsanity.com/paper/2301.05430/) (2023)
- [A Novel Approach to Temporal QoS Estimation via Extended Kalman Filter-Incorporated Latent Feature Analysis](https://mlsanity.com/paper/2606.23010/) (2026)
- [Compositional Coding for Collaborative Filtering](https://mlsanity.com/paper/1905.03752/) (2019)
- [EvoFed: Leveraging Evolutionary Strategies for Communication-Efficient Federated Learning](https://mlsanity.com/paper/2311.07485/) (2023)
- [Projected Hamming Dissimilarity for Bit-Level Importance Coding in Collaborative Filtering](https://mlsanity.com/paper/2103.14455/) (2021)
- [Efficient and Robust Regularized Federated Recommendation](https://mlsanity.com/paper/2411.01540/) (2024)
- [Proximal Symmetric Non-negative Latent Factor Analysis: A Novel Approach to Highly-Accurate Representation of Undirected Weighted Networks](https://mlsanity.com/paper/2306.03647/) (2023)
- [HCFRec: Hash Collaborative Filtering via Normalized Flow with Structural Consensus for Efficient Recommendation](https://mlsanity.com/paper/2205.12042/) (2022)

## Abstract (arXiv)

Hash Learning (HL) is an efficient representation learning approach that maps real-valued data into compact binary representations. Traditional HL methods typically require users to upload personal data to a central server, which is incompatible with increasingly stringent data security regulations. Federated Learning (FL) provides a decentralized paradigm for learning globally optimal models without centralizing private data. However, most FL methods rely on transmitting large-scale real-valued gradient information, leading to high communication overhead and potential privacy risks. Integrating HL into FL is a promising solution. Nevertheless, existing HL methods suffer from limited representational capacity of binary codes, which may degrade model accuracy. To address this challenge, we propose a Federated Hash Projected Latent Factor (FHPLF) model. FHPLF introduces three key innovations: (a) replacing real-valued gradient matrices with binary gradient-like matrices, significantly reducing computation, storage, and communication costs while enhancing privacy protection; (b) leveraging Projected Hamming Distance for similarity modeling, which captures the importance of individual binary bits to improve representation capability; and (c) proposing a Secure Binary Gradient Reassembly and Privacy-Enhanced Upload (SBG-PEU) strategy to further reduce the risk of user interaction leakage during transmission. Extensive experiments on four real-world datasets demonstrate that FHPLF consistently outperforms state-of-the-art HL and FL methods, achieving a favorable trade-off among accuracy, efficiency, and privacy preservation.