# Far From Sight, Far From Mind: Inverse Distance Weighting for Graph Federated Recommendation

arXiv: https://arxiv.org/abs/2507.01285 · HTML: https://mlsanity.com/paper/2507.01285/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 42 references that are in the map.
- First posted on arXiv 2 Jul 2025.

## Builds on (most cited first, 24 of 24)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates](https://mlsanity.com/paper/1803.01498/) (2018)
- [Neural Graph Collaborative Filtering](https://mlsanity.com/paper/1905.08108/) (2019)
- [Graph Neural Networks in Recommender Systems: A Survey](https://mlsanity.com/paper/2011.02260/) (2020)
- [Variational Autoencoders for Collaborative Filtering](https://mlsanity.com/paper/1802.05814/) (2018)
- [Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders](https://mlsanity.com/paper/2403.03952/) (2024)
- [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)
- [Federated Social Recommendation with Graph Neural Network](https://mlsanity.com/paper/2111.10778/) (2021)
- [Learning Private Neural Language Modeling with Attentive Aggregation](https://mlsanity.com/paper/1812.07108/) (2018)
- [Exploring Data Splitting Strategies for the Evaluation of Recommendation Models](https://mlsanity.com/paper/2007.13237/) (2020)
- [Federated Neural Collaborative Filtering](https://mlsanity.com/paper/2106.04405/) (2021)
- [Federated Multi-view Matrix Factorization for Personalized Recommendations](https://mlsanity.com/paper/2004.04256/) (2020)
- [A Survey on Federated Recommendation Systems](https://mlsanity.com/paper/2301.00767/) (2022)
- [Semi-decentralized Federated Ego Graph Learning for Recommendation](https://mlsanity.com/paper/2302.10900/) (2023)
- [On-Device Recommender Systems: A Comprehensive Survey](https://mlsanity.com/paper/2401.11441/) (2024)
- [Federated Heterogeneous Graph Neural Network for Privacy-preserving Recommendation](https://mlsanity.com/paper/2310.11730/) (2023)
- [GPFedRec: Graph-guided Personalization for Federated Recommendation](https://mlsanity.com/paper/2305.07866/) (2023)
- [Towards Personalized Privacy: User-Governed Data Contribution for Federated Recommendation](https://mlsanity.com/paper/2401.17630/) (2024)
- [Shared MF: A privacy-preserving recommendation system](https://mlsanity.com/paper/2008.07759/) (2020)
- [Practical and Secure Federated Recommendation with Personalized Masks](https://mlsanity.com/paper/2109.02464/) (2021)

## Abstract (arXiv)

Graph federated recommendation systems offer a privacy-preserving alternative to traditional centralized recommendation architectures, which often raise concerns about data security. While federated learning enables personalized recommendations without exposing raw user data, existing aggregation methods overlook the unique properties of user embeddings in this setting. Indeed, traditional aggregation methods fail to account for their complexity and the critical role of user similarity in recommendation effectiveness. Moreover, evolving user interactions require adaptive aggregation while preserving the influence of high-relevance anchor users (the primary users before expansion in graph-based frameworks). To address these limitations, we introduce Dist-FedAvg, a novel distance-based aggregation method designed to enhance personalization and aggregation efficiency in graph federated learning. Our method assigns higher aggregation weights to users with similar embeddings, while ensuring that anchor users retain significant influence in local updates. Empirical evaluations on multiple datasets demonstrate that Dist-FedAvg consistently outperforms baseline aggregation techniques, improving recommendation accuracy while maintaining seamless integration into existing federated learning frameworks.