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Found 17 papers, 4 papers with code
Evaluating Membership Inference Attacks and Defenses in Federated Learning
Firstly, combining model information from multiple communication rounds (Multi-temporal) enhances the overall effectiveness of MIAs compared to utilizing model information from a single epoch.
A Theoretical Analysis of Efficiency Constrained Utility-Privacy Bi-Objective Optimization in Federated Learning
Concerns about utility, privacy, and training efficiency in FL have garnered significant research attention.
Grounding Foundation Models through Federated Transfer Learning: A General Framework
Motivated by the strong growth in FTL-FM research and the potential impact of FTL-FM on industrial applications, we propose an FTL-FM framework that formulates problems of grounding FMs in the federated learning setting, construct a detailed taxonomy based on the FTL-FM framework to categorize state-of-the-art FTL-FM works, and comprehensively overview FTL-FM works based on the proposed taxonomy.
A Communication Theory Perspective on Prompting Engineering Methods for Large Language Models
The springing up of Large Language Models (LLMs) has shifted the community from single-task-orientated natural language processing (NLP) research to a holistic end-to-end multi-task learning paradigm.
Temporal Gradient Inversion Attacks with Robust Optimization
We investigate a Temporal Gradient Inversion Attack with a Robust Optimization framework, called TGIAs-RO, which recovers private data without any prior knowledge by leveraging multiple temporal gradients.
FedSOV: Federated Model Secure Ownership Verification with Unforgeable Signature
Federated learning allows multiple parties to collaborate in learning a global model without revealing private data.
FedPDD: A Privacy-preserving Double Distillation Framework for Cross-silo Federated Recommendation
However, in reality, the number of overlapped users is often very small, thus largely limiting the performance of such approaches.
FedZKP: Federated Model Ownership Verification with Zero-knowledge Proof
Federated learning (FL) allows multiple parties to cooperatively learn a federated model without sharing private data with each other.
Optimizing Privacy, Utility and Efficiency in Constrained Multi-Objective Federated Learning
Different from existing CMOFL works focusing on utility, efficiency, fairness, and robustness, we consider optimizing privacy leakage along with utility loss and training cost, the three primary objectives of a TFL system.
FedPass: Privacy-Preserving Vertical Federated Deep Learning with Adaptive Obfuscation
Vertical federated learning (VFL) allows an active party with labeled feature to leverage auxiliary features from the passive parties to improve model performance.
FedCut: A Spectral Analysis Framework for Reliable Detection of Byzantine Colluders
Extensive experimental results under a variety of settings justify the superiority of FedCut, which demonstrates extremely robust model performance (MP) under various attacks.
FedTracker: Furnishing Ownership Verification and Traceability for Federated Learning Model
To deter such misbehavior, it is essential to establish a mechanism for verifying the ownership of the model and as well tracing its origin to the leaker among the FL participants.
No Free Lunch Theorem for Security and Utility in Federated Learning
In a federated learning scenario where multiple parties jointly learn a model from their respective data, there exist two conflicting goals for the choice of appropriate algorithms.
FedIPR: Ownership Verification for Federated Deep Neural Network Models
To address these risks, the ownership verification of federated learning models is a prerequisite that protects federated learning model intellectual property rights (IPR) i. e., FedIPR.
Federated Deep Learning with Bayesian Privacy
To address the aforementioned perplexity, we propose a novel Bayesian Privacy (BP) framework which enables Bayesian restoration attacks to be formulated as the probability of reconstructing private data from observed public information.
Generative Adversarial Networks for Robust Cryo-EM Image Denoising
Equipped with robust $\ell_1$ Autoencoder and some designs of robust $\beta$-GANs, one can stabilize the training of GANs and achieve the state-of-the-art performance of robust denoising with low SNR data and against possible information contamination.
Data-Driven Tight Frame for Cryo-EM Image Denoising and Conformational Classification
We introduce the use of data-driven tight frame (DDTF) algorithm for cryo-EM image denoising.
Computation Image and Video Processing
