Jul 2026· International Journal of Information Security· Vol 25· 0 citations· 42 references
Computer Science
TL;DR
Empirical findings indicate that DFL-PHS, in many configurations, competes with or outperforms CFL, attaining F1-scores of 0.965 and recall rates surpassing 0.98, even under minimal weight-sharing conditions.
Abstract
Federated Learning (FL) enables participants to collaboratively train a model without sharing raw data. Traditional FL models depend on a central server and full model sharing, leading to a significant issue in healthcare related to privacy concerns and communication overhead. However, to address such issues, we introduce Decentralized Federated Learning with Partial Head Sharing DFL-PHS, a framework that eliminates the central server, allowing peer-to-peer model updates sharing. Instead of sharing entire model parameters, participants only share a subset of weights from the final dense layers, minimizing information breach while maintaining the model’s performance. We apply our proposed framework to COVID-19 chest X-ray binary classification, where we conduct a comparative study across four scenarios: local training, centralized FL (CFL), single-site training (SST) and DFL-PHS. Evaluations are conducted on three dataset sizes (small, medium, large) and four partial sharing ratios (25%, 50%, 75%, 100%). We also assess privacy through a Membership-Inference Attack (MIA), with only 25% head sharing, the attack operates at or below chance (ROC-AUC 0.37 with advantage -0.30 on the small scale and ROC–AUC ≈0.50\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\approx 0.50$$\end{document} with advantage ≈0.00\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\approx 0.00$$\end{document} on the large scale), indicating no reliable membership signal. Empirical findings indicate that DFL-PHS, in many configurations, competes with or outperforms CFL, attaining F1-scores of 0.965 and recall rates surpassing 0.98, even under minimal weight-sharing conditions. These results validate DFL-PHS as a privacy-preserving and scalable system for distributed FL. While evaluated on a single radiography dataset, our framework is dataset-agnostic; we plan broader validation on non-medical and other benchmarks as future work.
This work provides a unified approach for aiding the design of state-of-the-art privacy-preserving distributed learning systems that are also utility-optimal and is an important step towards using such approaches in high-stakes domains like healthcare or finance.
A. M., Nitish Kumar· International Journal of Mat...· 0 citations
This work introduces a novel multi-domain federated learning framework in which lightweight client-side proxy models collaborate with a server-side Foundation Model (FM) to learn new concepts without sharing private data.
Matteo Caligiuri, Francesco Barbato, Pietro Zanuttigh et al.· Trans. Mach. Learn. Res.· 0 citations
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
Graph neural networks (GNNs) have become a dominant paradigm for learning over graph-structured data. To protect data privacy in distributed graph settings, federated GNNs have emerged as a promising solution by enabling collaborative model training without raw data sharing. However, recent studies demonstrate that federated GNNs can inherit and even amplify biases from distributed data, resulting in unfair global models. While state-of-the-art (SOTA) approaches have introduced fairness-aware federated GNN frameworks, they overlook the privacy risks arising from client–server communications during training. To address this gap, we propose SaFeGNN, a Secure and Fair Federated Graph Neural Network framework that jointly enforces privacy protection and fairness guarantees. SaFeGNN secures the communication process via additive secret sharing and client-level differential privacy, achieving stronger security guarantees compared to existing solutions. Experimental results show that SaFeGNN maintains performance close to that of the baseline, with only an additional overhead of 2.7 s and 3.6 MB per global round.
Jia-Qiang Chen, Zhang He, Xiaoning Liu et al.· Journal of Cybersecurity and...· 0 citations
Clustered Federated Learning (CFL) addresses data heterogeneity in federated settings by grouping clients with similar data distributions to enable effective training. Existing methods face a trade-off between privacy preservation, communication cost, and computational efficiency. We formalize this as the CFL trilemma, according to which improving two of these dimensions comes at the expense of the third. A prominent paradigm relies on metadata (i.e., low-dimensional representations of client datasets shared with the server) to enable communication- and computation-efficient clustering. However, such approaches are not compatible with standard FL privacy-preserving mechanisms. To address this limitation, we propose FLAMECHE, which reformulates metadata-based CFL as a distributed Expectation-Maximization (EM) procedure, restricting server updates to additive operations while preserving efficiency. This design enables compatibility with practical secure FL schemes. We conducted extensive experiments on multiple datasets under various heterogeneous scenarios. Results show that FLAMECHE improves the effectiveness of client models. It enables encryption-compatible metadata-based clustering, enhancing its positioning within the CFL trilemma.
Michael Ben Ali, I. Megdiche, A. Péninou et al.· arXiv.org· 0 citations
Federated learning (FL) enables collaborative model training across multiple clients in a privacy-preserving manner. However, the employment of homomorphic encryption algorithms might lead to high computational cost while the application of differential privacy (DP) methods would sacrifice model performance. To establish efficient and secure FL system as well as maintaining competitive performance, we introduce a DP-enabled cascaded filter with novel model-aggregation mechanism. Taking the model parameters of one client for example, dimensions with large absolute values and significant variations are selected by the cascaded filter and regarded as important dimensions. After that, random noise are added to these important dimensions for data security. Finally, the model parameters will be sent to the central server for aggregation. Unlike traditional DP-based approaches, our method considers each dimension’s informational importance, i.e., both absolute value and its variation. We theoretically prove the convergence of our method and verify the effectiveness on different scenarios including four datasets. The experimental results suggest that our method outperforms the other ones from literature under both IID and non-IID conditions while achieving high level of efficiency and privacy protection performance.
Zhiqiang Chen, Yuchen Jiang, Ray Y. Zhong et al.· IEEE Transactions on Informa...· 0 citations
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