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Found 10 papers, 3 papers with code
Navigating Data Heterogeneity in Federated Learning A Semi-Supervised Federated Object Detection
Federated Learning (FL) has emerged as a potent framework for training models across distributed data sources while maintaining data privacy.
Does fine-tuning GPT-3 with the OpenAI API leak personally-identifiable information?
In this work, we simulate a privacy attack on GPT-3 using OpenAI's fine-tuning API.
Collusion Resistant Federated Learning with Oblivious Distributed Differential Privacy
Privacy-preserving federated learning enables a population of distributed clients to jointly learn a shared model while keeping client training data private, even from an untrusted server.
Gradient Masked Averaging for Federated Learning
A major challenge in federated learning is the heterogeneity of data across client, which can degrade the performance of standard FL algorithms.
Multi-VFL: A Vertical Federated Learning System for Multiple Data and Label Owners
Vertical Federated Learning (VFL) refers to the collaborative training of a model on a dataset where the features of the dataset are split among multiple data owners, while label information is owned by a single data owner.
Prior-Independent Auctions for the Demand Side of Federated Learning
Federated learning (FL) is a paradigm that allows distributed clients to learn a shared machine learning model without sharing their sensitive training data.
Bias-Free FedGAN: A Federated Approach to Generate Bias-Free Datasets
Our approach generates metadata at the aggregator using the models received from clients and retrains the federated model to achieve bias-free results for image synthesis.
DPD-InfoGAN: Differentially Private Distributed InfoGAN
The Information Maximizing GAN (InfoGAN) is a variant of the default GAN that introduces feature-control variables that are automatically learned by the framework, hence providing greater control over the different kinds of images produced.
BlockFLow: An Accountable and Privacy-Preserving Solution for Federated Learning
When run on the public Ethereum blockchain, BlockFLow uses the results from the audit to reward parties with cryptocurrency based on the quality of their contribution.
PrivacyFL: A simulator for privacy-preserving and secure federated learning
In this paper, we motivate our research, describe the architecture of the simulator and associated protocols, and discuss its evaluation in numerous scenarios that highlight its wide range of functionality and its advantages.
