FedFG introduces a robust federated learning framework using flow-matching generation to simultaneously enhance client privacy and defend against sophisticated poisoning attacks.

The paper proposes FLRSP, a privacy-preserving federated learning method that enhances robustness by randomly selecting model parameters for global model updates, maintaining high accuracy against sta…

This paper proposes and evaluates a federated deep learning framework using autoencoders for lightweight, privacy-preserving, and scalable real-time anomaly detection in resource-constrained IoT netwo…

The paper proposes FedCONST, an approach for federated learning that adaptively modulates update magnitudes to prevent overfitting and promote stronger generalization.

FedIDM introduces a novel federated learning framework that uses iterative distribution matching to achieve fast and stable convergence and maintain high model utility even when facing a large proport…

The paper proposes a novel decentralized framework that uses blockchain and Multi-task Peer Prediction to incentivize and manage the computationally intensive process of Federated Learning.

The paper proposes a proactive client selection framework that optimizes the selection of client subsets to ensure high data utility and fairness before federated learning begins, leading to faster an…

This paper proposes and evaluates the integration of Federated Learning and blockchain technology over cloud-edge infrastructure to enhance data privacy and security for decentralized AI applications.

This paper introduces the FedSaSync strategy for Semi-Asynchronous Federated Learning in the Flower framework, improving robustness and reducing idle time in heterogeneous environments.

FedMTFI introduces a novel federated learning framework that uses multi-teacher knowledge distillation and feature importance to improve model performance and robustness in heterogeneous and non-IID d…

The paper proposes AdaBFL, a multi-layer defensive adaptive aggregation method that enhances Byzantine-robust federated learning by adaptively adjusting defense weights to counter complex poisoning at…

This paper argues that much of the existing research on Federated Learning (FL) security is based on idealized assumptions, and provides a practical evaluation framework showing that real-world attack…

This paper introduces the first complete pipeline for federated unlearning, proposing an efficient unlearning approach and a novel visualization framework (Skyeye) to evaluate a model's forgetting cap…

This paper analyzes label inference attacks in Vertical Federated Learning (VFL), demonstrating that existing attacks rely on feature-label distribution alignment, and proposes a zero-overhead defense…

The paper introduces Dynamic Sharded Federated Learning (DSFL), a secure aggregation framework that significantly reduces communication overhead and enhances update verification for cross-institution…

This paper proposes a comprehensive federated learning workflow that enhances privacy and robustness by integrating personalized differential privacy budgets and client drift detection, achieving bett…

This paper investigates the effects of structural and temporal inhomogeneities in decentralised federated learning and shows that they significantly slow down the convergence process.

The paper proposes S2-WEF, a novel detection method that simulates potential global-model-based attacks to dynamically identify free-riding clients in Federated Learning, achieving high robustness aga…

The paper proposes DIST-FL, a distributed system using multiple TEEs and an append-only ledger to enhance the security and robustness of federated learning aggregation against server-side adversaries.

This paper proposes a framework for federated learning with limited client memory and time-varying sampling costs, and develops an Active-Constraint Drift-Plus-Penalty (ACDPP) policy to minimize the c…