Advances and open problems in federated learning

P Kairouz, HB McMahan, B Avent… - … and trends® in …, 2021 - nowpublishers.com
Federated learning (FL) is a machine learning setting where many clients (eg, mobile
devices or whole organizations) collaboratively train a model under the orchestration of a …

Federated learning vulnerabilities, threats and defenses: A systematic review and future directions

S Almutairi, A Barnawi - Internet of Things, 2023 - Elsevier
Today, a broad range of items, ranging from smartphones to smart cars are connected
together via the Internet, also known as the Internet of Things (IoT). The IoT is powered by …

Posthoc privacy guarantees for collaborative inference with modified Propose-Test-Release

A Singh, P Vepakomma, V Sharma… - Advances in Neural …, 2023 - proceedings.neurips.cc
Cloud-based machine learning inference is an emerging paradigm where users query by
sending their data through a service provider who runs an ML model on that data and …

Adasplit: Adaptive trade-offs for resource-constrained distributed deep learning

A Chopra, SK Sahu, A Singh, A Java… - arXiv preprint arXiv …, 2021 - arxiv.org
Distributed deep learning frameworks like federated learning (FL) and its variants are
enabling personalized experiences across a wide range of web clients and mobile/IoT …

Split HE: Fast secure inference combining split learning and homomorphic encryption

GL Pereteanu, A Alansary… - arXiv preprint arXiv …, 2022 - arxiv.org
This work presents a novel protocol for fast secure inference of neural networks applied to
computer vision applications. It focuses on improving the overall performance of the online …

[HTML][HTML] A comprehensive analysis of model poisoning attacks in federated learning for autonomous vehicles: A benchmark study

S Almutairi, A Barnawi - Results in Engineering, 2024 - Elsevier
Due to the increase in data regulations amid rising privacy concerns, the machine learning
(ML) community has proposed a novel distributed training paradigm called federated …

A Stealthy Wrongdoer: Feature-Oriented Reconstruction Attack against Split Learning

X Xu, M Yang, W Yi, Z Li, J Wang… - Proceedings of the …, 2024 - openaccess.thecvf.com
Split Learning (SL) is a distributed learning framework renowned for its privacy-preserving
features and minimal computational requirements. Previous research consistently highlights …

Context-aware hybrid encoding for privacy-preserving computation in IoT devices

H Khalili, HJ Chien, A Hass… - IEEE Internet of Things …, 2023 - ieeexplore.ieee.org
Recent years have witnessed a surge in hybrid IoT-cloud applications where an end user
distributes the desired computation between the IoT and cloud nodes. While achieving …

Decouple-and-sample: Protecting sensitive information in task agnostic data release

A Singh, E Garza, A Chopra, P Vepakomma… - … on Computer Vision, 2022 - Springer
We propose sanitizer, a framework for secure and task-agnostic data release. While
releasing datasets continues to make a big impact in various applications of computer …

GAN you see me? enhanced data reconstruction attacks against split inference

Z Li, M Yang, Y Liu, J Wang, H Hu… - Advances in Neural …, 2024 - proceedings.neurips.cc
Split Inference (SI) is an emerging deep learning paradigm that addresses computational
constraints on edge devices and preserves data privacy through collaborative edge-cloud …