Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications.
TLDR
A more thorough summary of the most relevant protocols, platforms, and real-life use-cases of FL is provided to enable data scientists to build better privacy-preserved solutions for industries in critical need of FL.Abstract:
This paper provides a comprehensive study of Federated Learning (FL) with an emphasis on enabling software and hardware platforms, protocols, real-life applications and use-cases. FL can be applicable to multiple domains but applying it to different industries has its own set of obstacles. FL is known as collaborative learning, where algorithm(s) get trained across multiple devices or servers with decentralized data samples without having to exchange the actual data. This approach is radically different from other more established techniques such as getting the data samples uploaded to servers or having data in some form of distributed infrastructure. FL on the other hand generates more robust models without sharing data, leading to privacy-preserved solutions with higher security and access privileges to data. This paper starts by providing an overview of FL. Then, it gives an overview of technical details that pertain to FL enabling technologies, protocols, and applications. Compared to other survey papers in the field, our objective is to provide a more thorough summary of the most relevant protocols, platforms, and real-life use-cases of FL to enable data scientists to build better privacy-preserving solutions for industries in critical need of FL. We also provide an overview of key challenges presented in the recent literature and provide a summary of related research work. Moreover, we explore both the challenges and advantages of FL and present detailed service use-cases to illustrate how different architectures and protocols that use FL can fit together to deliver desired results.read more
Citations
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Journal ArticleDOI
A survey on security and privacy of federated learning
Viraaji Mothukuri,Reza M. Parizi,Seyedamin Pouriyeh,Yan Huang,Ali Dehghantanha,Gautam Srivastava,Gautam Srivastava +6 more
TL;DR: This paper aims to provide a comprehensive study concerning FL’s security and privacy aspects that can help bridge the gap between the current state of federated AI and a future in which mass adoption is possible.
Journal ArticleDOI
Federated Learning for Internet of Things: A Comprehensive Survey
TL;DR: In this paper, a comprehensive survey of the emerging applications of federated learning in IoT networks is provided, which explores and analyzes 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.
Posted Content
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection
TL;DR: A comprehensive review of federated learning systems can be found in this paper, where the authors provide a thorough categorization of the existing systems according to six different aspects, including data distribution, machine learning model, privacy mechanism, communication architecture, scale of federation and motivation of federation.
Journal ArticleDOI
Federated Learning for Internet of Things: A Comprehensive Survey
TL;DR: In this paper, a comprehensive survey of the emerging applications of federated learning in IoT networks is provided, which explores and analyzes 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.
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Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges
TL;DR: The recent advances of federated learning towards enabling Federated learning-powered IoT applications are presented and a set of metrics such as sparsification, robustness, quantization, scalability, security, and privacy, is delineated in order to rigorously evaluate the recent advances.
References
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Proceedings ArticleDOI
CMFL: Mitigating Communication Overhead for Federated Learning
Luping Wang,Wei Wang,Bo Li +2 more
TL;DR: Communication-Mitigated Federated Learning provides clients with feedback information regarding the global tendency of model updating and can substantially reduce the communication overhead while still guaranteeing the learning convergence.
Posted Content
Practical Secure Aggregation for Federated Learning on User-Held Data
Keith Bonawitz,Vladimir Ivanov,Ben Kreuter,Antonio Marcedone,H. Brendan McMahan,Sarvar Patel,Daniel Ramage,Aaron Segal,Karn Seth +8 more
TL;DR: This work considers training a deep neural network in the Federated Learning model, using distributed stochastic gradient descent across user-held training data on mobile devices, wherein Secure Aggregation protects each user's model gradient.
Journal ArticleDOI
Multi-site fMRI analysis using privacy-preserving federated learning and domain adaptation: ABIDE results
TL;DR: The results demonstrate that it is promising to utilize multi-site data without data sharing to boost neuroimage analysis performance and find reliable disease-related biomarkers.
Proceedings ArticleDOI
DÏoT: A Federated Self-learning Anomaly Detection System for IoT
Thien Duc Nguyen,Samuel Marchal,Markus Miettinen,Hossein Fereidooni,Nadarajah Asokan,Ahmad-Reza Sadeghi +5 more
Abstract: IoT devices are increasingly deployed in daily life. Many of these devices are, however, vulnerable due to insecure design, implementation, and configuration. As a result, many networks already have vulnerable IoT devices that are easy to compromise. This has led to a new category of malware specifically targeting IoT devices. However, existing intrusion detection techniques are not effective in detecting compromised IoT devices given the massive scale of the problem in terms of the number of different types of devices and manufacturers involved. In this paper, we present DIoT, an autonomous self-learning distributed system for detecting compromised IoT devices. DIoT builds effectively on device-type-specific communication profiles without human intervention nor labeled data that are subsequently used to detect anomalous deviations in devices' communication behavior, potentially caused by malicious adversaries. DIoT utilizes a federated learning approach for aggregating behavior profiles efficiently. To the best of our knowledge, it is the first system to employ a federated learning approach to anomaly-detection-based intrusion detection. Consequently, DIoT can cope with emerging new and unknown attacks. We systematically and extensively evaluated more than 30 off-the-shelf IoT devices over a long term and show that DIoT is highly effective (95.6% detection rate) and fast (257 ms) at detecting devices compromised by, for instance, the infamous Mirai malware. DIoT reported no false alarms when evaluated in a real-world smart home deployment setting.
Posted Content
On the Convergence of Federated Optimization in Heterogeneous Networks.
TL;DR: This work proposes and introduces \fedprox, which is similar in spirit to \fedavg, but more amenable to theoretical analysis, and describes the convergence of \fed Prox under a novel \textit{device similarity} assumption.
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