FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing

Yin, Feng; Lin, Zhidi; Xu, Yue; Kong, Qinglei; Li, Deshi; Shuguang,

doi:10.48550/arxiv.2003.03697

Cited by 4 publications

(5 citation statements)

References 72 publications

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“…We hope that researchers in diverse FL applications can contribute more valuable models and realistic datasets to our community. Promising application domains include, but are not limited to, computer vision [77,78], natural language processing [79,80,81,82,83], finance [74,35,84], transportation [85,86,87,88,89,90,91,92,93,94], digital health [95,96,97,98,99,100,101], recommendation [102,103,104,105,106,107], robotics [108,109], and smart cities [110,111].…”

Section: Architecture Designmentioning

confidence: 99%

FedML: A Research Library and Benchmark for Federated Machine Learning

He,

Li,

et al. 2020

Preprint

116

159

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Federated learning is a rapidly growing research field in the machine learning domain. Although considerable research efforts have been made, existing libraries cannot adequately support diverse algorithmic development (e.g., diverse topology and flexible message exchange), and inconsistent dataset and model usage in experiments make fair comparisons difficult. In this work, we introduce FedML, an open research library and benchmark that facilitates the development of new federated learning algorithms and fair performance comparisons. FedML supports three computing paradigms (distributed training, mobile on-device training, and standalone simulation) for users to conduct experiments in different system environments. FedML also promotes diverse algorithmic research with flexible and generic API design and reference baseline implementations. A curated and comprehensive benchmark dataset for the non-I.I.D setting aims at making a fair comparison. We believe FedML can provide an efficient and reproducible means of developing and evaluating algorithms for the federated learning research community. We maintain the source code, documents, and user community at https://FedML.ai.

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Section: Architecture Designmentioning

confidence: 99%

FedML: A Research Library and Benchmark for Federated Machine Learning

He,

Li,

et al. 2020

Preprint

116

159

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“…An experiment is conduced in a laboratory corridor setting, indicating a high localization estimation accuracy with high security. Similarly, a Federated Localization (FedLoc) framework is considered in [91] for providing accurate localization services in IoT networks. By cooperating multiple users in the model learning using local fingerprint data, FL minimizes the bias of location estimation with privacy protections.…”

Section: Fl For Iot Localizationmentioning

confidence: 99%

Federated Learning for Internet of Things: A Comprehensive Survey

Nguyen,

Ding,

Pathirana

et al. 2021

Preprint

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The Internet of Things (IoT) is penetrating many facets of our daily life with the proliferation of intelligent services and applications empowered by artificial intelligence (AI). Traditionally, AI techniques require centralized data collection and processing that may not be feasible in realistic application scenarios due to the high scalability of modern IoT networks and growing data privacy concerns. Federated Learning (FL) has emerged as a distributed collaborative AI approach that can enable many intelligent IoT applications, by allowing for AI training at distributed IoT devices without the need for data sharing. In this article, we provide a comprehensive survey of the emerging applications of FL in IoT networks, beginning from an introduction to the recent advances in FL and IoT to a discussion of their integration. Particularly, we explore and analyze 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. We then provide an extensive survey of the use of FL in various key IoT applications such as smart healthcare, smart transportation, Unmanned Aerial Vehicles (UAVs), smart cities, and smart industry. The important lessons learned from this review of the FL-IoT services and applications are also highlighted. We complete this survey by highlighting the current challenges and possible directions for future research in this booming area.

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“…Predict quarantined and at-risk people's current location to enforce them stay at isolation/protection facility AI [343], [344] People/traffic density prediction Predict people density and traffic density Cellular, AI [128]- [132], [345]…”

Section: Incentivementioning

confidence: 99%

“…Mobile phones [342], [344], [361], Intelligent Transportation Systems [323], [324] RFID [76], [78], [80], [93] Active: 100m or more. Passive: ∼ 10m [91] Less than 1m [78] High Low Medium Indoor Mobile phones, smart home, smart city [94], [95] places and can accidentally infect others before know that they carry the disease.…”

Section: Medium Yes Outdoormentioning

confidence: 99%

“…Considering various channel models, i.e., path-loss, shadowing, and fading, the proposed solutions can achieve Neyman-Pearson optimal performance by observing the probability of missdetections and false-alarms. The authors in [344] propose a novel localization system leveraging a federated learning to allow mobile users to collaboratively provide accurate location services without revealing mobile users' private location. As such, the authors utilize deep neural networks with the Gaussian process to accurately predict the desired location of the mobile users.…”

Section: Medium Yes Outdoormentioning

confidence: 99%

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Enabling and Emerging Technologies for Social Distancing: A Comprehensive Survey and Open Problems

Nguyen,

Saputra,

Van Huynh

et al. 2020

Preprint

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Social distancing is crucial for preventing the spread of viral diseases illnesses such as COVID-19. By minimizing the closely physical contact between people, we can reduce chances of catching the virus and spreading it to the community. This paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, AI can enable, encourage, and even enforce social distancing. To that end, we provide a fundamental background of social distancing including basic concepts, measurements, models and propose practical scenarios. We then discuss enabling wireless technologies which are especially effective and can be widely adopted in practice to keep distance and monitor people. After that, emerging technologies such as machine learning, computer vision, thermal, ultrasound, etc., are introduced. These technologies open many new solutions and directions to deal with problems in social distancing, e.g., symptom prediction, detection and monitoring quarantined people, and contact tracing. Finally, we provide important open issues and challenges (e.g., privacy-preserving, cybersecurity) in implementing social distancing in practice.

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FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing

Cited by 4 publications

References 72 publications

FedML: A Research Library and Benchmark for Federated Machine Learning

FedML: A Research Library and Benchmark for Federated Machine Learning

Federated Learning for Internet of Things: A Comprehensive Survey

Enabling and Emerging Technologies for Social Distancing: A Comprehensive Survey and Open Problems

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