Aug 2026· International journal of computer information systems and industrial management applications· Vol 18, pp. 772-794· 0 citations
TL;DR
A federated learning, which is merged with GNN to allow decentralised training whilst maintaining the relational structure of data in terms of interaction, is proposed, which demonstrates the usefulness of federated graph-based learning in order to have secure and accurate recommendation systems.
Abstract
Recommender systems are common in today's online platforms to give personalised recommendations RS depending on the interaction of the users. Nevertheless, the conventional centralised recommendation methods demand gathering user information in central servers, which poses a serious privacy risk and a higher risk of information leakage. Concurrently, most of the traditional models do not reflect the structural relations that exist between the user and item interactions. In order to resolve these problems, this paper will suggest a FGNN framework, in which privacy-preserving recommendation is provided. The method proposed is a federated learning, which is merged with GNN to allow decentralised training whilst maintaining the relational structure of data in terms of interaction. All the clients build a local user-item graph and learn GNN-based embedding without distributing raw data. The aggregating server receives updates on the model by a federated averaging mechanism in order to construct a global model. The experimental results with Amazon review data indicate that the proposed federated GNN has a correlation of 0.9176, RMSE of 0.4021, and MAE of 0.2135, which is better than multiple centralised and base methods. Full privacy is also guaranteed by the model since the user data is not transferred to any other clients. These findings demonstrate the usefulness of federated graph-based learning in order to have secure and accurate recommendation systems.
Federated recommendation (FedRec) allows training recommender systems without collecting user data centrally[3]. However, in these settings, particularly in cross-device scenarios where each client corresponds to a single user, only local interaction histories are available. Consequently, the learning process relies on partial, fragmented, and sparse signals. This, in turn, makes collaboration between users indirect, with only a subset of users participating in each training round. In practice, this means the model is trained on incomplete and potentially biased data. Standard Federated Learning (FL) methods assume that simply averaging updates is enough, but in this setting, that is not sufficient to recover meaningful collaborative patterns. This thesis studies the following question: "How do federated constraints influence the propagation of collaborative signals in recommendation systems, and how can key mechanisms be redesigned to improve performance, fairness, and cross-domain transfer under realistic cross-device conditions? " To address this question, the thesis is structured around four interconnected research directions: Similarity-aware aggregation for personalized representations: Standard federated aggregation usually combines client updates uniformly or by data size, even when user representations reflect very different behavioral contexts. This can dilute useful personalization signals and amplify mismatched updates. This direction proposes distance-based aggregation, where user-specific parameters are weighted by representation similarity, with the goal of improving the performance of graph FedRec systems[2]. Controlled User Expansion and eligibility-aware client sampling : User expansion is a common method used in graph FedRec systems to leverage high order interactions and counteract the small local interaction graph each client has access to [4]. However, existing methods often rely on trusted third-party servers and introduce uncontrolled replication. This direction proposes a nearest neighbor based user expansion, and pairs it with a client selection strategy that account for both direct participation and indirect influence. The goal is to reduce over-representation of frequently selected clients and improve participation fairness. Bias and Fairness Analysis in FedRec : Fairness in centralized recommendation has been studied extensively, but it is less clear how FL settings change bias dynamics. Intermittent participation, non-IID data, and limited access to sensitive attributes may alter how disparities emerge and evolve. This direction compares centralized and FedRecs under aligned architectures and evaluation protocols to isolate the effect of FL on bias in recommendation. It also adapts fairness-aware training (based on [1]) so that group-level statistics can be estimated securely, without exposing sensitive information centrally. Structural bridging for cross-domain FedRec : Cross-domain recommendation in FL settings is especially difficult when overlap between domains is sparse [5]. In such cases, natural transfer paths are weak and knowledge sharing depends on very limited shared structure. In this direction we plan to explore synthetic bridge construction from semantic item representations to create additional cross-domain connections while preserving privacy. By selectively activating useful bridges and pruning harmful ones, the work aims to improve transfer in low-overlap regimes while keeping negative transfer low.
Aymen Rayane Khouas· Annual International ACM SIG...· 0 citations
Overview: Federated Learning (FL) is a type of machine learning in which multiple clients use their own data to train a model. This does not require sending the raw data from each client to a central server. However, FL is currently very vulnerable to several types of attacks, including inference, model inversion, and membership inference. In this study, we have implemented a comprehensive experimental framework for analysing FL performance using standard FL aggregation protocols FedAvg, FedProx, and SCAFFOLD in conjunction with Differential Privacy (DP) mechanisms; specifically, the Gaussian noise mechanism with Rényi Differential Privacy (RDP) accountants. Our experiments all used K = 100 simulated clients, with heterogeneous data distribution across the entire federation (non-IID), and the respective clean datasets used were MNIST and CIFAR-10. For example, in our IID scenarios, our DP-FedAvg protocol achieved an accuracy of 84.9% on MNIST with ε = 1.0 and δ = 10^-5 (i.e., no privacy guarantees and 92.6% reuse of the same parameters). This corresponds to a 7.7-percentage-point trade-off in accuracy for provable privacy. In our non-IID scenario, we achieved an accuracy of 72.8% for the exact same privacy parameters and an LDA αᴸᴸᴸ = 0.5. We systematically analyse how various combinations of clipping norms on gradients, noise multipliers, and client participation rates converged. All results have been reported as-is, along with the associated standard deviation across each of the five independent sample runs that we performed using different random seed values. Finally, our framework will assist practitioners deploying privacy-friendly FL within either healthcare, banking, or Internet of Things environments.
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, Zeyu Hao, Guoshuai Zhao et al.· Proceedings of the 32nd ACM...· 1 citation
The fast proliferation of data-driven applications and intelligent systems has raised problem awareness as far as the concerns regarding data privacy, data security, and regulatory compliance are concerned to a very high level. The conventional centralized machine learning models demand the coalescence of raw data situated in disseminated sources, which is a grave threat of information leaks, unauthorized data accessibility, and breaking a privacy policy like GDPR and HIPAA. Federated Learning (FL) has become a hopeful decentralized learning framework so as to facilitate joint model training among various customers without relocating crude data to a central point. Rather, model updates of the local models are shared and aggregated, hence retaining locality of data and improving privacy. This paper will provide an in-depth analysis of federated learning methods and pay special attention to the issue of privacy. Its research paper investigates the very principles of federated learning, architecture designs, communication scheme, and ways of aggregation. The diverse privacy-enhancing schemes are secure aggregation, differential privacy, homomorphic encryption, trusted execution environments, and are critically analyzed. Moreover, this article examines new developments, concerns and tradeoffs connected with privacy, effectiveness of communication, model noise, and scalability of systems. There is a comparative analysis of federated learning methods presented in organized tabular form and mathematical equations. The existing studies are discussed with their experimental results in order to outline the effectiveness of federated learning to maintain the privacy and achieve the acceptable model accuracy. Lastly, the research issues and future paths are outlined in order to direct the further development of the privacy-preserving federated learning systems.
Aarav Mehta· International Journal of App...· 0 citations
Federated learning is a decentralised machine-learning approach in which several clients jointly build a shared model without moving their raw data to one location. Rising concerns around privacy, tightening regulation, and restrictions on how data may be owned or shared have made this approach increasingly attractive in practice. Although federated learning lowers privacy exposure relative to centralised training, deploying it in practice is complicated by clients whose data are unevenly distributed and non-identically distributed, by clients that participate inconsistently, and by training that can converge unpredictably. To obtain global models that train reliably and consistently even when client data are heterogeneous, this work puts forward a federated learning system built around privacy preservation. The design follows a client–server pattern in which a coordinating server aggregates updates from local models using weights that account for imbalance among participants. The behaviour of the resulting system is examined methodically across several data-distribution regimes — IID, mildly non-IID, and severely non-IID. The experiments show that the framework converges reliably and delivers predictive accuracy that holds up well, especially in the more difficult non-IID cases. Compared with conventional federated learning baselines, the approach shows greater robustness and steadier performance across successive training rounds. Because it is simple to implement, repeatable, and built with real deployment in mind, the architecture suits privacy-sensitive, decentralised use cases such as distributed intelligent systems, industrial monitoring, and healthcare analytics.
Shyam Patel, S. Khan· 2026 International Conferenc...· 0 citations
FedSTAR is proposed, a privacy-preserving cross-border recommendation framework that integrates spatio-temporal dynamic modeling with federated graph neural networks and delivers both high accuracy and strong robustness, offering a secure and practically viable solution for cross-border recommendation.