# Sharpness-Aware Minimization for Generalized Embedding Learning in Federated Recommendation

arXiv: https://arxiv.org/abs/2603.11503 · HTML: https://mlsanity.com/paper/2603.11503/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 64 references that are in the map.
- First posted on arXiv 12 Mar 2026.

## Builds on (most cited first, 27 of 27)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Sharpness-Aware Minimization for Efficiently Improving Generalization](https://mlsanity.com/paper/2010.01412/) (2020)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [Deep Learning based Recommender System: A Survey and New Perspectives](https://mlsanity.com/paper/1707.07435/) (2017)
- [On the properties of variational approximations of Gibbs posteriors](https://mlsanity.com/paper/1506.04091/) (2015)
- [Secure Federated Matrix Factorization](https://mlsanity.com/paper/1906.05108/) (2019)
- [Towards Understanding Generalization of Deep Learning: Perspective of Loss Landscapes](https://mlsanity.com/paper/1706.10239/) (2017)
- [FastPillars: A Deployment-friendly Pillar-based 3D Detector](https://mlsanity.com/paper/2302.02367/) (2023)
- [Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systems](https://mlsanity.com/paper/2210.10629/) (2022)
- [Federated Neural Collaborative Filtering](https://mlsanity.com/paper/2106.04405/) (2021)
- [Understanding the Dynamics of DNNs Using Graph Modularity](https://mlsanity.com/paper/2111.12485/) (2021)
- [Personalized Federated Recommendation via Joint Representation Learning, User Clustering, and Model Adaptation](https://mlsanity.com/paper/2208.09375/) (2022)
- [A Survey on Federated Recommendation Systems](https://mlsanity.com/paper/2301.00767/) (2022)
- [Swift Sampler: Efficient Learning of Sampler by 10 Parameters](https://mlsanity.com/paper/2410.05578/) (2024)
- [FS-Real: Towards Real-World Cross-Device Federated Learning](https://mlsanity.com/paper/2303.13363/) (2023)
- [GPFedRec: Graph-guided Personalization for Federated Recommendation](https://mlsanity.com/paper/2305.07866/) (2023)
- [Learn from Global Correlations: Enhancing Evolutionary Algorithm via Spectral GNN](https://mlsanity.com/paper/2412.17629/) (2024)
- [PerFedRec++: Enhancing Personalized Federated Recommendation with Self-Supervised Pre-Training](https://mlsanity.com/paper/2305.06622/) (2023)
- [From Points to Coalitions: Hierarchical Contrastive Shapley Values for Prioritizing Data Samples](https://mlsanity.com/paper/2512.19363/) (2025)
- [CEC-Zero: Zero-Supervision Character Error Correction with Self-Generated Rewards](https://mlsanity.com/paper/2512.23971/) (2025)
- [Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approach](https://mlsanity.com/paper/2408.08931/) (2024)
- [Federated Graph Learning for Cross-Domain Recommendation](https://mlsanity.com/paper/2410.08249/) (2024)
- [Co-clustering for Federated Recommender System](https://mlsanity.com/paper/2411.01690/) (2024)
- [Decentralized Graph Neural Network for Privacy-Preserving Recommendation](https://mlsanity.com/paper/2308.08072/) (2023)
- [A Tutorial of Personalized Federated Recommender Systems: Recent Advances and Future Directions](https://mlsanity.com/paper/2412.08071/) (2024)
- [RAID: An In-Training Defense against Attribute Inference Attacks in Recommender Systems](https://mlsanity.com/paper/2504.11510/) (2025)

## Abstract (arXiv)

Federated recommender systems enable collaborative model training while keeping user interaction data local and sharing only essential model parameters, thereby mitigating privacy risks. However, existing methods overlook a critical issue, i.e., the stable learning of a generalized item embedding throughout the federated recommender system training process. Item embedding plays a central role in facilitating knowledge sharing across clients. Yet, under the cross-device setting, local data distributions exhibit significant heterogeneity and sparsity, exacerbating the difficulty of learning generalized embeddings. These factors make the stable learning of generalized item embeddings both indispensable for effective federated recommendation and inherently difficult to achieve. To fill this gap, we propose a new federated recommendation framework, named Federated Recommendation with Generalized Embedding Learning (FedRecGEL). We reformulate the federated recommendation problem from an item-centered perspective and cast it as a multi-task learning problem, aiming to learn generalized embeddings throughout the training procedure. Based on theoretical analysis, we employ sharpness-aware minimization to address the generalization problem, thereby stabilizing the training process and enhancing recommendation performance. Extensive experiments on four datasets demonstrate the effectiveness of FedRecGEL in significantly improving federated recommendation performance. Our code is available at https://github.com/anonymifish/FedRecGEL.