Corpus ID: 216144848

A Review of Privacy Preserving Federated Learning for Private IoT Analytics

@article{Briggs2020ARO,
  title={A Review of Privacy Preserving Federated Learning for Private IoT Analytics},
  author={Christopher Briggs and Zhong Fan and P{\'e}ter Andr{\'a}s},
  journal={ArXiv},
  year={2020},
  volume={abs/2004.11794}
}
The Internet-of-Things generates vast quantities of data, much of it attributable to an individual's activity and behaviour. Holding and processing such personal data in a central location presents a significant privacy risk to individuals (of being identified or of their sensitive data being leaked). However, analytics based on machine learning and in particular deep learning benefit greatly from large amounts of data to develop high performance predictive models. Traditionally, data and… Expand
Federated Learning on Non-IID Data: A Survey
TLDR
A detailed analysis of the influence of Non-IID data on both parametric and non-parametric machine learning models in both horizontal and vertical federated learning is provided. Expand
FedAdapt: Adaptive Offloading for IoT Devices in Federated Learning
TLDR
FedAdapt, an adaptive offloading FL framework that accelerates local training in computationally constrained devices by leveraging layer offloading of deep neural networks (DNNs) to servers, and is demonstrated to reduce the training time by up to 40% when compared to classic FL, without sacrificing accuracy. Expand
Blockchain for federated learning toward secure distributed machine learning systems: a systemic survey
  • Dun Li, Dezhi Han, +5 authors Kuan-Ching Li
  • Medicine
  • Soft computing
  • 2021
Federated learning (FL) is a promising decentralized deep learning technology, which allows users to update models cooperatively without sharing their data. FL is reshaping existing industryExpand
Federated Semi-Supervised Multi-Task Learning to Detect COVID-19 and Lungs Segmentation Marking Using Chest Radiography Images and Raspberry Pi Devices: An Internet of Medical Things Application
TLDR
This study explores two such medical decision-making tasks, namely COVID-19 detection and lung area segmentation detection, using chest radiography images, and investigates the applicability of computationally less capable edge devices in the IoMT system. Expand
Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challenges
TLDR
Several main issues in FLchain design are identified, including communication cost, resource allocation, incentive mechanism, security and privacy protection, and the applications of FLchain in popular MEC domains, such as edge data sharing, edge content caching and edge crowdsensing are investigated. Expand
Federated Learning for Internet of Things: A Comprehensive Survey
TLDR
This article explores the potential of FL for enabling a wide range of IoT services, including IoT data sharing, data offloading and caching, attack detection, localization, mobile crowdsensing, and IoT privacy and security. Expand

References

SHOWING 1-10 OF 111 REFERENCES
Federated Learning with Non-IID Data
TLDR
This work presents a strategy to improve training on non-IID data by creating a small subset of data which is globally shared between all the edge devices, and shows that accuracy can be increased by 30% for the CIFAR-10 dataset with only 5% globally shared data. Expand
Mitigating Sybils in Federated Learning Poisoning
TLDR
FoolsGold is described, a novel defense to this problem that identifies poisoning sybils based on the diversity of client updates in the distributed learning process that exceeds the capabilities of existing state of the art approaches to countering sybil-based label-flipping and backdoor poisoning attacks. Expand
How To Backdoor Federated Learning
TLDR
This work designs and evaluates a new model-poisoning methodology based on model replacement and demonstrates that any participant in federated learning can introduce hidden backdoor functionality into the joint global model, e.g., to ensure that an image classifier assigns an attacker-chosen label to images with certain features. Expand
Differentially Private Federated Learning: A Client Level Perspective
TLDR
The aim is to hide clients' contributions during training, balancing the trade-off between privacy loss and model performance, and empirical studies suggest that given a sufficiently large number of participating clients, this procedure can maintain client-level differential privacy at only a minor cost in model performance. Expand
Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data
TLDR
Sparse ternary compression (STC) is proposed, a new compression framework that is specifically designed to meet the requirements of the federated learning environment and advocate for a paradigm shift in federated optimization toward high-frequency low-bitwidth communication, in particular in the bandwidth-constrained learning environments. Expand
Privacy-preserving deep learning
  • R. Shokri, Vitaly Shmatikov
  • Computer Science
  • 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton)
  • 2015
TLDR
This paper presents a practical system that enables multiple parties to jointly learn an accurate neural-network model for a given objective without sharing their input datasets, and exploits the fact that the optimization algorithms used in modern deep learning, namely, those based on stochastic gradient descent, can be parallelized and executed asynchronously. Expand
Scalable Private Learning with PATE
TLDR
This work shows how PATE can scale to learning tasks with large numbers of output classes and uncurated, imbalanced training data with errors, and introduces new noisy aggregation mechanisms for teacher ensembles that are more selective and add less noise, and prove their tighter differential-privacy guarantees. Expand
Federated Optimization: Distributed Machine Learning for On-Device Intelligence
We introduce a new and increasingly relevant setting for distributed optimization in machine learning, where the data defining the optimization are unevenly distributed over an extremely large numberExpand
FADL: Federated-Autonomous Deep Learning for Distributed Electronic Health Record
TLDR
It is shown, using ICU data from 58 different hospitals, that machine learning models to predict patient mortality can be trained efficiently without moving health data out of their silos using a distributed machine learning strategy. Expand
Human Activity Recognition Using Federated Learning
  • Konstantin Sozinov, Vladimir Vlassov, Sarunas Girdzijauskas
  • Computer Science
  • 2018 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communications (ISPA/IUCC/BDCloud/SocialCom/SustainCom)
  • 2018
TLDR
It is found that federated learning for the task of human activity recognition is capable of producing models with slightly worse, but acceptable, accuracy compared to centralized models and a trade-off between communication cost and the complexity of a model is identified. Expand
...
1
2
3
4
5
...