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

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 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.

The paper proposes Jellyfish, a zero-shot federated unlearning scheme that effectively removes the influence of forgotten data from federated learning models while maintaining model utility and privac…

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…

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…

The paper identifies a new class of difficult-to-detect trustworthiness failures, termed 'Silent Failures,' that arise when personalizing foundation models using federated learning, arguing that curre…

This paper proposes a framework for reward allocation in AI cooperatives using value-conditioned gradient filtering, online marginal contribution signals, and cumulative revenue settlement within a tr…

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…

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

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.

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 Federated Adversarial Unlearning (FAUN), a lightweight framework that uses adversarial optimization on a proxy dataset to rapidly and effectively remove the negative impact of poiso…

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…

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

The paper proposes Byz-Clip21-SGD2M, a novel algorithm that achieves high-probability convergence guarantees for Federated Learning by integrating robust aggregation, double momentum, and clipping, re…

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…