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Entropy
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Advancing Privacy-Preserving Federated Learning: Innovative Frameworks and Protocols
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Advancing Privacy-Preserving Federated Learning: Innovative Frameworks and Protocols

A Special Issue of Entropy (ISSN 1099-4300).

Deadline for manuscript submissions: 15 January 2027 | Viewed by 4066

Editors

Dr. Lihua Wang

E-Mail Website1 Website2
Guest Editor
Cybersecurity Research Institute, National Institute of Information and Communications Technology (NICT), Koganei, Tokyo 184-8795, Japan
Interests: applied cryptography; privacy-preserving machine learning; secure data utilization
Prof. Dr. Rongxing Lu

E-Mail Website
Guest Editor
School of Computing, Queen's University, Kingston, ON K7L 2N8, Canada
Interests: privacy enhancing technologies; IoT-big data security and privacy; applied cryptography
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The increasing demand for data privacy and secure machine learning has fueled interest in federated learning (FL)—a paradigm that enables the training of machine learning models across distributed devices and servers without centralized data collection. This aligns closely with the core themes of Entropy, including information-theoretic security, uncertainty quantification, and the trade-offs among privacy, utility, and efficiency. Despite its promise, federated learning still faces critical challenges in guaranteeing robust privacy in the context of realistic adversarial models. This Special Issue invites high-quality, original research that advances the theoretical foundations and practical implementations of privacy-preserving federated learning, grounded in information theory, entropy-based analysis, and cryptographic mechanisms. We especially welcome interdisciplinary contributions that explore the intersection of machine learning, cryptography, and information theory. Studies that provide measurable security guarantees, resource-efficient protocols, and scalable system designs are of particular interest. 
Topics of interest include, but are not limited to, the following:

  • Novel protocols for privacy-preserving federated learning;
  • Information-theoretic and entropy-based approaches to privacy in FL;
  • Practical frameworks combining distributed machine learning and cryptography;
  • Integration of homomorphic encryption, secure multiparty computation, and differential privacy in decentralized learning;
  • Blockchain and distributed ledger technologies for enhancing trust and accountability in FL;
  • Theoretical analysis of privacy–utility trade-offs;
  • Secure aggregation and model update strategies;
  • System architectures and real-world deployments of privacy-preserving FL;
  • Benchmarking, evaluation metrics, and performance analysis of privacy-enhanced federated systems;
    The submission of manuscripts that address related advancements in privacy-preserving techniques for other distributed or collaborative learning paradigms is also encouraged. We look forward to receiving your contributions.

Dr. Lihua Wang
Prof. Dr. Rongxing Lu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • federated learning
  • privacy preserving
  • homomorphic encryption
  • differential privacy
  • secure multiparty computation
  • privacy-enhancing technologies
  • distributed AI
  • applied cryptography

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Published Papers (5 papers)

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Research

21 pages, 711 KB  
Article
FedTIP: Communication-Efficient Federated Temporal Prompting for Few-Shot Dynamic Graph Adaptation
by Xijun Wu and Xinming Zhang
Entropy 2026, 28(9), 989; https://doi.org/10.3390/e28090989 (registering DOI) - 4 Sep 2026
Abstract
Temporal interaction streams are often distributed across data owners, while downstream labels and communication budgets remain limited. Existing temporal graph prompts typically adapt at a centralized trainer, whereas federated graph methods commonly exchange larger trainable states. This paper presents FedTIP, a prompt-level federated [...] Read more.
Temporal interaction streams are often distributed across data owners, while downstream labels and communication budgets remain limited. Existing temporal graph prompts typically adapt at a centralized trainer, whereas federated graph methods commonly exchange larger trainable states. This paper presents FedTIP, a prompt-level federated method for client-local few-shot adaptation of temporal interaction graphs. A centralized historical stage constructs a temporal knowledge bank whose encoder is frozen during downstream federation. Clients optimize interaction-conditioned prompts on local supervised events and transmit a compact prompt-side message. Prototype anchoring and a reliability rule based on support coverage and update magnitude combine heterogeneous client updates, while prompt-side proximal regularization and stage-scoped negative sampling preserve the temporal protocol. Experiments on Wikipedia, Reddit, and MOOC cover temporal node classification and transductive and inductive link prediction. FedTIP records the highest mean AUC-ROC in the nine reported task–dataset cells, with gains of 1.83–14.69 points over the strongest federated baseline in each cell. Its measured cumulative float32 uplink is 0.40 MiB per downstream task, 64.6–99.6% below the evaluated baselines. Full article
(This article belongs to the Special Issue Advancing Privacy-Preserving Federated Learning: Innovative Frameworks and Protocols)
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28 pages, 3181 KB  
Article
FedVI: Financial Cross-Domain Federated Learning with Scarce Overlapping Samples via Visual Representation of Heterogeneous Tabular Data and Meta-Optimization
by Kaiqing Yuan and Jiang Wu
Entropy 2026, 28(6), 637; https://doi.org/10.3390/e28060637 - 4 Jun 2026
Viewed by 621
Abstract
Federated learning offers a promising approach for cross-institutional financial risk control modeling but encounters two key challenges in practice: feature space heterogeneity and low sample overlap rate. Current federated transfer learning methods often rely heavily on sufficient overlapping samples or explicit feature alignment. [...] Read more.
Federated learning offers a promising approach for cross-institutional financial risk control modeling but encounters two key challenges in practice: feature space heterogeneity and low sample overlap rate. Current federated transfer learning methods often rely heavily on sufficient overlapping samples or explicit feature alignment. However, these approaches frequently result in negative transfer when enforced alignment is applied in highly heterogeneous environments. To address this issue, we propose FedVI, a novel federated transfer learning framework that integrates tabular-to-image conversion and meta-learning mechanisms. Moving beyond conventional methods that rely on sample-level alignment, FedVI employs a federated dual-stream feature alignment strategy to securely reconstruct a unified global feature map across institutions. Subsequently, FedVI integrates federated Image Generator for Tabular Data (IGTD) with tabular Transformer technology to convert one-dimensional tabular data into two-dimensional visual-semantic tensors. These tensors effectively fuse spatial topology and semantic information while embedding an independent Mask channel to explicitly retain the true missingness patterns of features. Finally, FedVI adopts the Model-Agnostic Meta-Learning (MAML) architecture to facilitate global parameter optimization. We evaluated FedVI on the real-world Lending Club credit dataset and Home Credit Default Risk datasets under highly heterogeneous federated settings (i.e., heterogeneous feature spaces across three clients and scarce overlapping samples). The results reveal that FedVI achieves competitive performance against advanced baselines such as FedProx, FedRep, and FedKT, particularly in recall and F1-Score. These findings indicate that FedVI can effectively support cross-domain adaptation under heterogeneous federated learning settings. Full article
(This article belongs to the Special Issue Advancing Privacy-Preserving Federated Learning: Innovative Frameworks and Protocols)
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28 pages, 1287 KB  
Article
DAG-CTFL: DAG Blockchain Cross-Layer Authentication Framework for Trustworthy IoV Federated Learning
by Longxia Liao and Long Chen
Entropy 2026, 28(6), 589; https://doi.org/10.3390/e28060589 - 26 May 2026
Viewed by 296
Abstract
Privacy-preserving federated learning in the internet of vehicles (IoV) requires low-latency authentication, bounded privacy leakage, and robustness against malicious model updates. However, most existing studies separately design communication authentication and federated learning protection, which leads to duplicated overhead and weak resistance to cross-layer [...] Read more.
Privacy-preserving federated learning in the internet of vehicles (IoV) requires low-latency authentication, bounded privacy leakage, and robustness against malicious model updates. However, most existing studies separately design communication authentication and federated learning protection, which leads to duplicated overhead and weak resistance to cross-layer attacks. To address this issue, this paper proposes a DAG blockchain-enabled cross-layer authentication framework for trustworthy IoV federated learning (DAG-CTFL). The framework reuses authentication operations across V2X message verification and model-update delivery, incorporates trust-aware batch verification, and organizes cross-layer evidence through a two-tier DAG blockchain. In addition, differential privacy is used to reduce information leakage from uploaded model updates, while cross-layer trust evaluation improves resilience against poisoning and forged-identity attacks. Experimental results on MNIST and CIFAR-10 show that DAG-CTFL reduces single-message verification overhead by 8.2–56.1%, lowers batch-verification latency by 19.2–56.4%, and maintains model accuracy above 85% under 15% malicious nodes. These results demonstrate that DAG-CTFL achieves an effective balance among privacy preservation, authentication efficiency, and cross-layer robustness in IoV federated learning. Full article
(This article belongs to the Special Issue Advancing Privacy-Preserving Federated Learning: Innovative Frameworks and Protocols)
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18 pages, 639 KB  
Article
Efficient Non-Interactive Discrete ReLU over CKKS Using Interpolation Look-Up Table
by Zhigang Chen, Xinxia Song and Liqun Chen
Entropy 2026, 28(5), 542; https://doi.org/10.3390/e28050542 - 11 May 2026
Viewed by 490
Abstract
Deploying neural networks on encrypted data requires efficient evaluation of nonlinear activations, especially the ReLU function, without decryption. While the CKKS homomorphic encryption scheme supports packed arithmetic over approximate numbers efficiently, its approximate semantics make direct nonlinear evaluation difficult, and polynomial surrogates often [...] Read more.
Deploying neural networks on encrypted data requires efficient evaluation of nonlinear activations, especially the ReLU function, without decryption. While the CKKS homomorphic encryption scheme supports packed arithmetic over approximate numbers efficiently, its approximate semantics make direct nonlinear evaluation difficult, and polynomial surrogates often introduce approximation error and non-discrete outputs. In this work, we present a task-specific, non-interactive construction for discrete ReLU evaluation in CKKS by combining modulus-switch-based discretization with interpolation-driven lookup-table (LUT) evaluation. We instantiate this design in two complementary schemes. The first uses trigonometric Hermite interpolation and functional bootstrapping to compute a discrete sign indicator, which is then combined with the encrypted input through conditional multiplication to obtain the ReLU output; this variant is compact and suitable for lightweight settings. The second uses iterative most-significant-bit (MSB) bootstrapping to support larger plaintext moduli and higher-precision regimes through repeated digit extraction. A common enabler of both schemes is a discretization step that maps approximate CKKS plaintexts to a finite integer representation; exactness in our setting therefore refers to exact evaluation over this discretized representation, while the deviation from the original CKKS plaintext is governed by the discretization error analyzed in Lemma 1. Experiments on encrypted MNIST inference and the accompanying LUT/storage analysis indicate that the proposed schemes preserve competitive accuracy relative to polynomial-approximation baselines while maintaining manageable auxiliary storage under the reported parameter settings. These results suggest that interpolation-based discrete activation is a promising alternative to polynomial approximation for selected CKKS-based encrypted inference tasks. Full article
(This article belongs to the Special Issue Advancing Privacy-Preserving Federated Learning: Innovative Frameworks and Protocols)
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29 pages, 473 KB  
Article
FedHGPrompt: Privacy-Preserving Federated Prompt Learning for Few-Shot Heterogeneous Graph Learning
by Xijun Wu, Jianjun Shi and Xinming Zhang
Entropy 2026, 28(2), 143; https://doi.org/10.3390/e28020143 - 27 Jan 2026
Viewed by 1271
Abstract
Learning from heterogeneous graphs under the constraints of both data scarcity and data privacy presents a significant challenge. While graph prompt learning offers a pathway for efficient few-shot adaptation, and federated learning provides a paradigm for decentralized training, their direct integration for heterogeneous [...] Read more.
Learning from heterogeneous graphs under the constraints of both data scarcity and data privacy presents a significant challenge. While graph prompt learning offers a pathway for efficient few-shot adaptation, and federated learning provides a paradigm for decentralized training, their direct integration for heterogeneous graphs is non-trivial due to structural complexity and the need for rigorous privacy guarantees. This paper proposes FedHGPrompt, a novel federated framework that bridges this gap through a cohesive architectural design. Our approach introduces a three-layer model: a unification layer employing dual templates to standardize heterogeneous graphs and tasks, an adaptation layer utilizing trainable dual prompts to steer a frozen pre-trained model for few-shot learning, and a privacy layer integrating a cryptographic secure aggregation protocol. This design ensures that the central server only accesses aggregated updates, thereby cryptographically safeguarding individual client data. Extensive evaluations on three real-world heterogeneous graph datasets (ACM, DBLP, and Freebase) demonstrate that FedHGPrompt achieves superior few-shot learning performance compared to existing federated graph learning baselines (including FedGCN, FedGAT, FedHAN, and FedGPL) while maintaining strong privacy assurances and practical communication efficiency. The framework establishes an effective approach for collaborative learning on distributed, heterogeneous graph data where privacy is paramount. Full article
(This article belongs to the Special Issue Advancing Privacy-Preserving Federated Learning: Innovative Frameworks and Protocols)
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