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Found 23 papers, 11 papers with code
Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs
Then, we propose a simple and effective framework called Graph Chain-of-thought (Graph-CoT) to augment LLMs with graphs by encouraging LLMs to reason on the graph iteratively.
FedSelect: Personalized Federated Learning with Customized Selection of Parameters for Fine-Tuning
Recent methods addressed the client data heterogeneity issue via personalized federated learning (PFL) - a class of FL algorithms aiming to personalize learned global knowledge to better suit the clients' local data distributions.
TablePuppet: A Generic Framework for Relational Federated Learning
In this paper, we formalize this problem as relational federated learning (RFL).
Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression
While state-of-the-art (SoTA) compression methods boast impressive advancements in preserving benign task performance, the potential risks of compression in terms of safety and trustworthiness have been largely neglected.
Differentially Private Synthetic Data via Foundation Model APIs 2: Text
Lin et al. (2024) recently introduced the Private Evolution (PE) algorithm to generate DP synthetic images with only API access to diffusion models.
Effective and Efficient Federated Tree Learning on Hybrid Data
To address this, we propose HybridTree, a novel federated learning approach that enables federated tree learning on hybrid data.
Ring-A-Bell! How Reliable are Concept Removal Methods for Diffusion Models?
While efforts have been made to mitigate such problems, either by implementing a safety filter at the evaluation stage or by fine-tuning models to eliminate undesirable concepts or styles, the effectiveness of these safety measures in dealing with a wide range of prompts remains largely unexplored.
DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models
Yet, while the literature on the trustworthiness of GPT models remains limited, practitioners have proposed employing capable GPT models for sensitive applications such as healthcare and finance -- where mistakes can be costly.
FedMLSecurity: A Benchmark for Attacks and Defenses in Federated Learning and Federated LLMs
This paper introduces FedSecurity, an end-to-end benchmark designed to simulate adversarial attacks and corresponding defense mechanisms in Federated Learning (FL).
PerAda: Parameter-Efficient Federated Learning Personalization with Generalization Guarantees
In this paper, we propose PerAda, a parameter-efficient pFL framework that reduces communication and computational costs and exhibits superior generalization performance, especially under test-time distribution shifts.
Privacy of Autonomous Vehicles: Risks, Protection Methods, and Future Directions
However, training AVs usually requires a large amount of training data collected from different driving environments (e. g., cities) as well as different types of personal information (e. g., working hours and routes).
Unraveling the Connections between Privacy and Certified Robustness in Federated Learning Against Poisoning Attacks
We then provide two robustness certification criteria: certified prediction and certified attack inefficacy for DPFL on both user and instance levels.
FOCUS: Fairness via Agent-Awareness for Federated Learning on Heterogeneous Data
However, due to the heterogeneous nature of local data, it is challenging to optimize or even define fairness of the trained global model for the agents.
UniFed: All-In-One Federated Learning Platform to Unify Open-Source Frameworks
The platform streamlines the end-to-end workflow for distributed experimentation and deployment, encompassing 11 popular open-source FL frameworks.
Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMM
To address these challenges, in this paper, we introduce a VFL framework with multiple heads (VIM), which takes the separate contribution of each client into account, and enables an efficient decomposition of the VFL optimization objective to sub-objectives that can be iteratively tackled by the server and the clients on their own.
Certified Robustness for Free in Differentially Private Federated Learning
Federated learning (FL) provides an efficient training paradigm to jointly train a global model leveraging data from distributed users.
RVFR: Robust Vertical Federated Learning via Feature Subspace Recovery
Vertical Federated Learning (VFL) is a distributed learning paradigm that allows multiple agents to jointly train a global model when each agent holds a different subset of features for the same sample(s).
Subnet Replacement: Deployment-stage backdoor attack against deep neural networks in gray-box setting
We study the realistic potential of conducting backdoor attack against deep neural networks (DNNs) during deployment stage.
CRFL: Certifiably Robust Federated Learning against Backdoor Attacks
Our method exploits clipping and smoothing on model parameters to control the global model smoothness, which yields a sample-wise robustness certification on backdoors with limited magnitude.
Style-based Point Generator with Adversarial Rendering for Point Cloud Completion
In this paper, we proposed a novel Style-based Point Generator with Adversarial Rendering (SpareNet) for point cloud completion.
Ranked #1 on
Point Cloud Completion
on ShapeNet
(Earth Mover's Distance metric)
Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses
As machine learning systems grow in scale, so do their training data requirements, forcing practitioners to automate and outsource the curation of training data in order to achieve state-of-the-art performance.
DBA: Distributed Backdoor Attacks against Federated Learning
Compared to standard centralized backdoors, we show that DBA is substantially more persistent and stealthy against FL on diverse datasets such as finance and image data.
Attack-Resistant Federated Learning with Residual-based Reweighting
Federated learning has a variety of applications in multiple domains by utilizing private training data stored on different devices.
