14,482 research outputs found
FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System
Federated Learning trains machine learning models on distributed devices by
aggregating local model updates instead of local data. However, privacy
concerns arise as the aggregated local models on the server may reveal
sensitive personal information by inversion attacks. Privacy-preserving
methods, such as homomorphic encryption (HE), then become necessary for FL
training. Despite HE's privacy advantages, its applications suffer from
impractical overheads, especially for foundation models. In this paper, we
present FedML-HE, the first practical federated learning system with efficient
HE-based secure model aggregation. FedML-HE proposes to selectively encrypt
sensitive parameters, significantly reducing both computation and communication
overheads during training while providing customizable privacy preservation.
Our optimized system demonstrates considerable overhead reduction, particularly
for large foundation models (e.g., ~10x reduction for ResNet-50, and up to ~40x
reduction for BERT), demonstrating the potential for scalable HE-based FL
deployment
PrivacyFL: A simulator for privacy-preserving and secure federated learning
Federated learning is a technique that enables distributed clients to
collaboratively learn a shared machine learning model while keeping their
training data localized. This reduces data privacy risks, however, privacy
concerns still exist since it is possible to leak information about the
training dataset from the trained model's weights or parameters. Setting up a
federated learning environment, especially with security and privacy
guarantees, is a time-consuming process with numerous configurations and
parameters that can be manipulated. In order to help clients ensure that
collaboration is feasible and to check that it improves their model accuracy, a
real-world simulator for privacy-preserving and secure federated learning is
required. In this paper, we introduce PrivacyFL, which is an extensible, easily
configurable and scalable simulator for federated learning environments. Its
key features include latency simulation, robustness to client departure,
support for both centralized and decentralized learning, and configurable
privacy and security mechanisms based on differential privacy and secure
multiparty computation. 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. Our paper addresses a significant real-world problem:
checking the feasibility of participating in a federated learning environment
under a variety of circumstances. It also has a strong practical impact because
organizations such as hospitals, banks, and research institutes, which have
large amounts of sensitive data and would like to collaborate, would greatly
benefit from having a system that enables them to do so in a privacy-preserving
and secure manner
Centralised rehearsal of decentralised cooperation: Multi-agent reinforcement learning for the scalable coordination of residential energy flexibility
This paper investigates how deep multi-agent reinforcement learning can
enable the scalable and privacy-preserving coordination of residential energy
flexibility. The coordination of distributed resources such as electric
vehicles and heating will be critical to the successful integration of large
shares of renewable energy in our electricity grid and, thus, to help mitigate
climate change. The pre-learning of individual reinforcement learning policies
can enable distributed control with no sharing of personal data required during
execution. However, previous approaches for multi-agent reinforcement
learning-based distributed energy resources coordination impose an ever greater
training computational burden as the size of the system increases. We therefore
adopt a deep multi-agent actor-critic method which uses a \emph{centralised but
factored critic} to rehearse coordination ahead of execution. Results show that
coordination is achieved at scale, with minimal information and communication
infrastructure requirements, no interference with daily activities, and privacy
protection. Significant savings are obtained for energy users, the distribution
network and greenhouse gas emissions. Moreover, training times are nearly 40
times shorter than with a previous state-of-the-art reinforcement learning
approach without the factored critic for 30 homes
NVIDIA FLARE: Federated Learning from Simulation to Real-World
Federated learning (FL) enables building robust and generalizable AI models
by leveraging diverse datasets from multiple collaborators without centralizing
the data. We created NVIDIA FLARE as an open-source software development kit
(SDK) to make it easier for data scientists to use FL in their research and
real-world applications. The SDK includes solutions for state-of-the-art FL
algorithms and federated machine learning approaches, which facilitate building
workflows for distributed learning across enterprises and enable platform
developers to create a secure, privacy-preserving offering for multiparty
collaboration utilizing homomorphic encryption or differential privacy. The SDK
is a lightweight, flexible, and scalable Python package. It allows researchers
to apply their data science workflows in any training libraries (PyTorch,
TensorFlow, XGBoost, or even NumPy) in real-world FL settings. This paper
introduces the key design principles of NVFlare and illustrates some use cases
(e.g., COVID analysis) with customizable FL workflows that implement different
privacy-preserving algorithms.
Code is available at https://github.com/NVIDIA/NVFlare.Comment: Accepted at the International Workshop on Federated Learning, NeurIPS
2022, New Orleans, USA (https://federated-learning.org/fl-neurips-2022);
Revised version v2: added Key Components list, system metrics for homomorphic
encryption experiment; Extended v3 for journal submissio
DeepSecure: Scalable Provably-Secure Deep Learning
This paper proposes DeepSecure, a novel framework that enables scalable
execution of the state-of-the-art Deep Learning (DL) models in a
privacy-preserving setting. DeepSecure targets scenarios in which neither of
the involved parties including the cloud servers that hold the DL model
parameters or the delegating clients who own the data is willing to reveal
their information. Our framework is the first to empower accurate and scalable
DL analysis of data generated by distributed clients without sacrificing the
security to maintain efficiency. The secure DL computation in DeepSecure is
performed using Yao's Garbled Circuit (GC) protocol. We devise GC-optimized
realization of various components used in DL. Our optimized implementation
achieves more than 58-fold higher throughput per sample compared with the
best-known prior solution. In addition to our optimized GC realization, we
introduce a set of novel low-overhead pre-processing techniques which further
reduce the GC overall runtime in the context of deep learning. Extensive
evaluations of various DL applications demonstrate up to two
orders-of-magnitude additional runtime improvement achieved as a result of our
pre-processing methodology. This paper also provides mechanisms to securely
delegate GC computations to a third party in constrained embedded settings
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