privacy-preserving

This paper proposes Hierarchical Federated Transfer Learning (HFTL) for Digital Twin-based Vehicular Ad hoc Networks, addressing data heterogeneity and sparsity via vehicle clustering and a data quality score mechanism to defend against malicious vehicles.

This paper presents the first systematic study of federated training for SpeechLLM-based end-to-end ASR systems, evaluating on English and Italian tasks and achieving competitive word error rates with reduced communication costs.

DP-FedSOFIM moves curvature estimation to the server in differentially private federated learning, achieving the same privacy guarantee as DP-FedGD with O(d) client memory and significant early-round accuracy gains.

The paper proposes OrchNAS, an energy-aware personalized federated edge intelligence framework that uses a Neural Architecture Search service to automatically design service-adaptive models for heterogeneous edge environments, addressing energy constraints and statistical heterogeneity.

OpenWorker is an open-source AI agent that performs tasks like drafting documents, sending messages, and managing calendars, using local data and multi-model support.

Introduces RELIC, a framework for learning interpretable and composable skills in multi-agent planning via revealed principles, enabling privacy-preserving coordination and cross-agent skill transfer without sharing code.

A systematic survey of Federated Explainable Artificial Intelligence (FedXAI), covering roles, architectures, evaluation practices, and open challenges. It presents a multi-axis taxonomy and discusses model-agnostic to interpretable-by-design approaches, highlighting gaps in standardization and privacy-aware evaluation.

Proposes FedCMM, a framework for federated continual learning of multimodal LLMs that uses modality-aware elastic weight consolidation, local generative replay, and task-similarity-aware gradient aggregation to mitigate catastrophic forgetting.

This paper proposes causal workloads—differentially private query sets based on orthogonal moments—to enable valid causal inference from synthetic data, introducing methods like Causal-AIM and noise-aware multiple imputation.

FedOPAL proposes a framework that adapts visual prompts as feature rectifiers for one-shot federated learning, achieving efficient gradient-free aggregation via analytic methods while outperforming existing analytical approaches and matching iterative methods with zero server-side training costs.

PRoVeFL is a novel federated learning framework that achieves privacy-preserving, Byzantine-robust, and verifiable aggregation using multi-key fully homomorphic encryption, offering up to 100× runtime improvement over prior works.

StepFM is a foundation model that uses only step counter data for broad-spectrum health prediction, offering a privacy-preserving and scalable alternative to high-frequency sensor models.

This paper analyzes the effect of structural and temporal heterogeneities in decentralized federated learning over temporal networks, showing that ignoring these heterogeneities leads to unrealistically rapid convergence and that real-world networks slow down diffusion.

Applies federated learning to object detection for drone fleets, enabling collaborative training without centralizing aerial imagery, achieving performance close to centralized training while preserving privacy and reducing bandwidth.

This paper introduces FoGS, a filtered mixture-of-generators pipeline that selects synthetic samples from multiple generative models to improve survival analysis training, outperforming real-data training on many datasets while preserving privacy.

TDGT is a web-based toolkit for synthetic tabular data generation that introduces the Adaptive Bayesian Mixture Synthesizer (ABMS) algorithm and a hybrid VAE-ABMS model, with GPU acceleration and comprehensive fidelity assessment.

This paper proposes a Federated Hash Projected Latent Factor (FHPLF) model that integrates hash learning into federated learning to reduce communication costs and enhance privacy, using binary gradient-like matrices and projected Hamming distance to improve accuracy and efficiency.

This paper systematically evaluates three survival models (Cox, DeepSurv, RSF) under federated learning on heterogeneous breast cancer data, finding that FL outperforms local training and RSF offers the best balance of performance across clients.

This survey provides a systematic review of federated causal discovery and inference, organizing methods by methodological paradigm, federation topology, and structural scope, and highlighting open challenges.

PSyGenTAB is a privacy-preserving framework that uses constrained optimization to generate synthetic clinical tabular data, balancing privacy and utility while preserving clinical relationships and minority-class patterns.