Showing 1–20 of 33 results
/ Date/ Name
Apr 29, 2020Privacy-Preserving Distributed Optimization via Subspace Perturbation: A General FrameworkNov 11, 2024ProP: Efficient Backdoor Detection via Propagation Perturbation for Overparametrized ModelsDec 13, 2023Adaptive Differentially Quantized Subspace Perturbation (ADQSP): A Unified Framework for Privacy-Preserving Distributed Average ConsensusSep 2, 2020Privacy-Preserving Distributed Processing: Metrics, Bounds, and AlgorithmsSep 21, 2024Re-Evaluating Privacy in Centralized and Decentralized Learning: An Information-Theoretical and Empirical StudyMay 30, 2021Communication efficient privacy-preserving distributed optimization using adaptive differential quantizationSep 21, 2024Perfect Gradient Inversion in Federated Learning: A New Paradigm from the Hidden Subset Sum ProblemDec 13, 2023Topology-Dependent Privacy Bound For Decentralized Federated LearningDec 13, 2023On the privacy of federated Clustering: A Cryptographic ViewDec 13, 2023Privacy-Preserving Distributed Optimisation using Stochastic PDMMAug 1, 2024ADBM: Adversarial diffusion bridge model for reliable adversarial purificationMar 17, 2026SOMP: Scalable Gradient Inversion for Large Language Models via Subspace-Guided Orthogonal Matching PursuitSep 11, 2024An Intelligent Innovation Dataset on Scientific Research OutcomesSep 16, 2024Privacy-Preserving Distributed Maximum Consensus Without Accuracy LossMar 10, 2025Trustworthy Machine Learning via Memorization and the Granular Long-Tail: A Survey on Interactions, Tradeoffs, and BeyondMay 21, 2025LAGO: Few-shot Crosslingual Embedding Inversion Attacks via Language Similarity-Aware Graph OptimizationMar 13, 2025Optimal Privacy-Preserving Distributed Median ConsensusJan 7, 2026Do LLMs Really Memorize Personally Identifiable Information? Revisiting PII Leakage with a Cue-Controlled Memorization FrameworkJul 12, 2024Provable Privacy Advantages of Decentralized Federated Learning via Distributed OptimizationMar 10, 2025From Centralized to Decentralized Federated Learning: Theoretical Insights, Privacy Preservation, and Robustness Challenges