privacy-preserving

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.

This paper introduces DiSan, a privacy-preserving text sanitization framework for distributed agent collaboration. By disentangling source-invariant role content from source-identifying style, DiSan reduces PII exposure 20× while maintaining 83% answer faithfulness on a multi-agent RAG benchmark, outperforming traditional masking approaches.

MedLatentDx proposes a latent multi-agent communication framework for cross-hospital rare-disease diagnosis, using latent KV blocks to share diagnostic evidence without exposing clinical text, and introduces the CrossRare-Bench benchmark.

This paper provides a comprehensive survey of Federated Continual Learning (FCL), an emerging field that combines Federated Learning and Continual Learning to enable lifelong, adaptive, and privacy-preserving learning over distributed and non-stationary data. It proposes a taxonomy, reviews applications, metrics, and open challenges.

This paper presents a vision for integrating multi-modal multi-task federated foundation models (M3T FedFMs) into vehicular networks, discussing training principles, use cases, challenges, and a case study on the Waymo Open Dataset.

InfoShield introduces a privacy-preserving method for speech representations in mental health screening using information-theoretic optimization, reducing sensitive attribute inference while maintaining diagnostic accuracy. A novel TimeAwareMINE estimator addresses temporal-static misalignment in sequential speech.

This paper proposes a locally deployed RAG-based academic advising system that combines large language models with retrieval from structured syllabus data to support course selection and personalized study planning in a privacy-preserving manner.