SOTAVerified

Privacy Preserving

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Showing 651–675 of 2975 papers

TitleStatusHype
Resource-Constrained Federated Continual Learning: What Does Matter?—0
Privacy-Preserving Model and Preprocessing Verification for Machine Learning—0
Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning—0
UFGraphFR: An attempt at a federated recommendation system based on user text characteristicsCode0
EVA-S2PLoR: A Secure Element-wise Multiplication Meets Logistic Regression on Heterogeneous Database—0
TAPFed: Threshold Secure Aggregation for Privacy-Preserving Federated Learning—0
Forecasting Anonymized Electricity Load Profiles—0
Federated Fine-Tuning of LLMs: Framework Comparison and Research Directions—0
Privacy-Preserving Distributed Online Mirror Descent for Nonconvex Optimization—0
Reach Measurement, Optimization and Frequency Capping In Targeted Online Advertising Under k-Anonymity—0
Navigating the Designs of Privacy-Preserving Fine-tuning for Large Language Models—0
VerifBFL: Leveraging zk-SNARKs for A Verifiable Blockchained Federated Learning—0
Decentralised Resource Sharing in TinyML: Wireless Bilayer Gossip Parallel SGD for Collaborative Learning—0
Lossless Privacy-Preserving Aggregation for Decentralized Federated Learning—0
FedKD-hybrid: Federated Hybrid Knowledge Distillation for Lithography Hotspot DetectionCode0
Optimizing Value of Learning in Task-Oriented Federated Meta-Learning Systems—0
The Robustness of Spiking Neural Networks in Federated Learning with Compression Against Non-omniscient Byzantine Attacks—0
From Models to Network Topologies: A Topology Inference Attack in Decentralized Federated Learning—0
Efficient Deployment of Large Language Models on Resource-constrained Devices—0
Towards the Anonymization of the Language Modeling—0
Unlearning through Knowledge Overwriting: Reversible Federated Unlearning via Selective Sparse Adapter—0
Fortifying Federated Learning Towards Trustworthiness via Auditable Data Valuation and Verifiable Client Contribution—0
pFedMxF: Personalized Federated Class-Incremental Learning with Mixture of Frequency Aggregation—0
Gaussian Splatting Feature Fields for (Privacy-Preserving) Visual Localization—0
Distinguish Then Exploit: Source-free Open Set Domain Adaptation via Weight Barcode Estimation and Sparse Label Assignment—0
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