Aug 2026· Journal of Intelligence and Information Systems· 0 citations· 30 references
TL;DR
A model-agnostic framework that forms personalized item embeddings through Long-Horizon Local Optimization and injects common global knowledge through intermittent Regularized Knowledge Guidance is proposed and Adaptive Guidance is introduced to control the influence of global knowledge at the user–item interaction level.
Abstract
Federated Recommendation (FedRec) enables privacy-preserving recommendation by training models across user devices without sharing raw interaction data. However, personalized FedRec still faces a trade-off between personalization and resource efficiency. In this paper, we revisit personalization from the perspective of the local optimization horizon. We find that sufficiently long local optimization can form user-specific item structures, but frequent synchronization or full replacement can disrupt them. Based on this observation, we propose
FedRKG
, a model-agnostic framework that forms personalized item embeddings through
Long-Horizon Local Optimization
and injects common global knowledge through intermittent
Regularized Knowledge Guidance
. This design incorporates shared collaborative signals without replacing local embeddings or maintaining dual representations. We further introduce
Adaptive Guidance
to control the influence of global knowledge at the user–item interaction level. Extensive experiments show that FedRKG consistently outperforms state-of-the-art FedRec baselines while reducing storage and communication costs. Our code is available at
https://github.com/Jaehyung-Lim/FedRKG_JIIS26_SCIE
.
PMFRec learns a personalized representation generator to produce user-specific item representations from attribute features, and introduces a global multi-view encoder with item-adaptive gating and an orthogonality objective to capture complementary semantic views while reducing cross-view redundancy.
Jaehyung Lim, Wonbin Kweon, Woojoo Kim et al.· 0 citations
With the increasing demand for privacy protection, the federated recommendation (FedRec) has become a critical research topic. Prevailing frameworks continuously decouple item representation to bypass the heterogeneity of client data, sacrificing the universality of aggregated item embeddings, losing the collaborative benefits of federated learning. This trend not only introduces prohibitive memory costs on resource-constrained edge devices, but also exacerbates the isolation of user interests, leading to a significant loss of recommendation diversity. In this paper, we attribute the failure of aggregated item embeddings to the server-side Global Geometric Misalignment and client-side Local Filtering Bubbles after empirical and theoretical analysis. Then, we propose CanonFedRec, a novel framework that resolves the above challenges through a canonical geometric perspective. First, we shift the optimization landscape from Euclidean space to a unit hypersphere to explicitly decouple local magnitude noise unrelated to popularity from semantic relationships. To rectify the global geometric inconsistency, we propose a Riemannian Projection-based Aggregation mechanism that performs global updates in a geometrically compatible tangent space. Furthermore, to mitigate the local filtering bubbles of clients, we propose a Divergence-Aware Elastic Alignment mechanism. This treats item-wise variance as a proxy for client-side cognitive divergence, and dynamically adapting optimization objectives via elastic decision boundaries. Extensive experiments on four datasets demonstrate that CanonFedRec not only significantly enhances the representational power of aggregated item embeddings, but also achieves superior performance while reducing memory costs by up to 40× compared to the best FedRec approach.
Yunqi Mi, Ze-Yu Hao, Guoshuai Zhao et al.· Proceedings of the 32nd ACM...· 1 citation
Federated learning (FL) is a practical framework that can train models on distributed user data while guaranteeing data privacy; however, due to heterogeneity in which each user has a different data distribution, problems frequently arise where both global and personalization performance deteriorate simultaneously. This dissertation presents methodologies for building efficient personalized models by identifying which strategies are effective in the global training stage and by showing how to preserve global knowledge while securing user-specific performance during local adaptation. First, we show that as data heterogeneity increases, the collapse of feature vectors is a more fundamental bottleneck than classifier weights, and propose a method that directly mitigates the discrepancy in representation magnitude between local and global models. Second, we analyze that a training approach that strengthens local alignment can induce forgetting of global knowledge (e.g., categories not observed locally), and propose a method that achieves both local alignment and global knowledge preservation by combining feature distillation based on the global model's feature vectors. Third, in federated personalized reward model learning with preference heterogeneity, we empirically verify the conventional belief that"increasing the number of global models yields better initialization,"and we show that when sufficient local fine-tuning is allowed, a single global initialization can instead provide stronger personalization performance. This study redefines the role of global initialization under data and preference heterogeneity and provides practical training strategies that simultaneously satisfy global knowledge preservation and personalization.
Federated Learning (FL) allows decentralized clients to train models collaboratively while preserving data privacy. However, distribution mismatch across clients often leads to poor global generalization and degraded local client-level performance. In such scenarios, some of the clients with their local models trained solely on local data may perform better than the globally learnt model, thus nullifying the benefits of collaborative federated learning. To address this, we propose SAPE-FL (Similarity-Aware Personalized Federated Learning), a novel personalization framework that anchors each client's model to both the global model and a similarity-weighted peer averaged model. By incorporating dynamic, client-specific regularization based on both model similarity and output similarity, SAPE-FL adaptively balances global knowledge transfer and peer collaboration while filtering out dissimilar clients. This dual anchoring mitigates negative transfer and enhances robustness in heterogeneous settings. We theoretically analyze our algorithm establishing its convergence guarantees and empirically show that SAPE-FL outperforms state-of-the-art methods under high statistical heterogeneity and low client data regimes.
A. Kumar, Sunil Gupta, Ngyuen Dang et al.· 0 citations
Federated recommendation enables collaborative model training while keeping user interaction data on local clients. A central problem in federated recommendation is how to aggregate useful information across clients for personalized recommendation. Existing personalized aggregation methods usually construct client relations from predefined parameter-based assumptions, such as parameter similarity or complementarity, and use these relations to determine aggregation weights. However, such methods construct a single global relation, which is insufficient to capture the hierarchical and multi-granularity nature of user relations in recommendation. Moreover, these predefined relations cannot directly reflect whether the related clients can improve prediction performance after aggregation. To address these limitations, we propose FedHUR, a federated recommendation framework for learning hierarchical utility-guided client relations. FedHUR takes item-item filters as the object for relation construction and aggregation. Specifically, it first aggregates and clusters each client's local information to obtain global hierarchical information. Each client computes hierarchical utility signals based on its local information and the global hierarchical information, indicating which collaborative information is useful for improving its prediction. The server uses these utility signals to retrieve clients that are useful to that client for further personalized aggregation. Extensive experiments on five real-world datasets show that FedHUR consistently outperforms existing federated recommendation baselines, demonstrating the effectiveness of hierarchical utility-guided client relation learning. Code is available at https://github.com/Mingzhe-Han/FedHUR.
Ming-Zhe Han, Jia-Hao Liu, Dong-Sheng Li et al.· 0 citations
This paper proposes a robust decentralized personalized federated learning method R-DPFL, that enables clients to reduce the impact of Byzantine attacks via robust neighborhood direction estimation and history-based update trend prediction, rather than purely aggregating client models as in the existing work. In R-DPFL, each client first computes the current-round model update by aggregating the received neighborhood update vectors. It then predicts what this update should be based on its historical values and local model changes. Finally, R-DPFL computes the difference between these two quantities, adaptively clips this difference, and adds it to the local update. We prove convergence of the learning process through rigorous analysis and show that honest clients maintain stable personalized descent dynamics under Byzantine neighbor perturbations without requiring consensus among neighboring models. Extensive experiments on CIFAR-10 demonstrate that RDPFL consistently outperforms state-of-the-art decentralized and personalized federated learning baselines under heterogeneous and adversarial settings.
Xiao Ma, Hong Shen, Hui Tian et al.· 1 citation
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