Privacy-Preserving Federated Learning in Fog Computing

Zhou, Chunyi; Fu, Anmin; Yu, Shui; Yang, Wei; Wang, Huaqun; Zhang, Yuqing

doi:10.1109/jiot.2020.2987958

Cited by 196 publications

(71 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Thus, IoT units communicate with each other in a p2p fashion, and, following a similar process as that described above, after a certain rounds of model exchanges and updates, they obtain a finally consensual model [ 7 ]. Each of the mentioned strategies, centralized and decentralized, are related to different networking architectures, which follow different paradigms, such as edge [ 14 ] or fog [ 15 ] computing, respectively. Figure 2 presents the architecture of both the approaches.…”

Section: Background: Enabling Technologiesmentioning

confidence: 99%

LPWAN and Embedded Machine Learning as Enablers for the Next Generation of Wearable Devices

Sánchez-Iborra¹

2021

Sensors

View full text Add to dashboard Cite

The penetration of wearable devices in our daily lives is unstoppable. Although they are very popular, so far, these elements provide a limited range of services that are mostly focused on monitoring tasks such as fitness, activity, or health tracking. Besides, given their hardware and power constraints, wearable units are dependent on a master device, e.g., a smartphone, to make decisions or send the collected data to the cloud. However, a new wave of both communication and artificial intelligence (AI)-based technologies fuels the evolution of wearables to an upper level. Concretely, they are the low-power wide-area network (LPWAN) and tiny machine-learning (TinyML) technologies. This paper reviews and discusses these solutions, and explores the major implications and challenges of this technological transformation. Finally, the results of an experimental study are presented, analyzing (i) the long-range connectivity gained by a wearable device in a university campus scenario, thanks to the integration of LPWAN communications, and (ii) how complex the intelligence embedded in this wearable unit can be. This study shows the interesting characteristics brought by these state-of-the-art paradigms, concluding that a wide variety of novel services and applications will be supported by the next generation of wearables.

show abstract

Section: Background: Enabling Technologiesmentioning

confidence: 99%

LPWAN and Embedded Machine Learning as Enablers for the Next Generation of Wearable Devices

Sánchez-Iborra¹

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…However, the system transfers data over the network, allowing the leakage of critical data [ 31 ] and the increasing risk of side-channel attack [ 32 ]. The edge computing framework utilizes federated learning technology, prevents direct access to the data, moves the compute resource to the edge, and prevents the raw data exchange to the central server [ 33 , 34 , 35 , 36 ]. For example, a smartphone that collects location data allows weather forecasting applications to directly access the user’s location, which violates information-based privacy.…”

Section: Related Workmentioning

confidence: 99%

Federated Compressed Learning Edge Computing Framework with Ensuring Data Privacy for PM2.5 Prediction in Smart City Sensing Applications

Putra

Chen

Prayitno

et al. 2021

Sensors

View full text Add to dashboard Cite

The sparse data in PM2.5 air quality monitoring systems is frequently happened on large-scale smart city sensing applications, which is collected via massive sensors. Moreover, it could be affected by inefficient node deployment, insufficient communication, and fragmented records, which is the main challenge of the high-resolution prediction system. In addition, data privacy in the existing centralized air quality prediction system cannot be ensured because the data which are mined from end sensory nodes constantly exposed to the network. Therefore, this paper proposes a novel edge computing framework, named Federated Compressed Learning (FCL), which provides efficient data generation while ensuring data privacy for PM2.5 predictions in the application of smart city sensing. The proposed scheme inherits the basic ideas of the compression technique, regional joint learning, and considers a secure data exchange. Thus, it could reduce the data quantity while preserving data privacy. This study would like to develop a green energy-based wireless sensing network system by using FCL edge computing framework. It is also one of key technologies of software and hardware co-design for reconfigurable and customized sensing devices application. Consequently, the prototypes are developed in order to validate the performances of the proposed framework. The results show that the data consumption is reduced by more than 95% with an error rate below 5%. Finally, the prediction results based on the FCL will generate slightly lower accuracy compared with centralized training. However, the data could be heavily compacted and securely transmitted in WSNs.

show abstract

“…Among them, multiple works focus on the integration of DP and federated learning. However, most of them (e.g., [8], [9], [10], [11], [12], [13], [14], [15], [16], [17]) demonstrate the performance of proposed approaches merely by experiments, with no theoretical analysis on the convergence. However, experimental observations are not always reliable since the performance of machine learning algorithms heavily rely on hyper-parameter tuning, and an algorithm that is observed to perform better than other algorithms may just be the algorithm that is better tuned.…”

Section: Related Workmentioning

confidence: 99%

“…In order to motivate and retain edge devices in federated learning, it is desirable to provide rigorous differential privacy (DP) guarantee for devices. While there have been multiple works focusing on the integration of DP and federated learning [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], most of the work demonstrate the performance of proposed approaches by experiments, whose results heavily rely on hyper-parameter tuning. The main focus of this paper is to provide a differentially-private federated learning approach with convergence performance bound.…”

Section: Introductionmentioning

confidence: 99%

Concentrated Differentially Private Federated Learning With Performance Analysis

Guo

2021

IEEE Open J. Comput. Soc.

View full text Add to dashboard Cite

Federated learning engages a set of edge devices to collaboratively train a common model without sharing their local data and has advantage in user privacy over traditional cloud-based learning approaches. However, recent model inversion attacks and membership inference attacks have demonstrated that shared model updates during the interactive training process could still leak sensitive user information. Thus, it is desirable to provide rigorous differential privacy (DP) guarantee in federated learning. The main challenge to providing DP is to maintain high utility of federated learning model with repeatedly introduced randomness of DP mechanisms, especially when the server is not fully trusted. In this paper, we investigate how to provide DP to the most widely adopted federated learning scheme, federated averaging. Our approach combines local gradient perturbation, secure aggregation, and zero-concentrated differential privacy (zCDP) for better utility and privacy protection without a trusted server. We jointly consider the performance impacts of randomness introduced by DP mechanisms, client sampling and data subsampling in our approach, and theoretically analyze the convergence rates and end-to-end DP guarantee with non-convex loss functions. We also demonstrate that our proposed method has good utility-privacy trade-off through extensive numerical experiments on a real-world dataset.

show abstract

Privacy-Preserving Federated Learning in Fog Computing

Cited by 196 publications

References 31 publications

LPWAN and Embedded Machine Learning as Enablers for the Next Generation of Wearable Devices

LPWAN and Embedded Machine Learning as Enablers for the Next Generation of Wearable Devices

Federated Compressed Learning Edge Computing Framework with Ensuring Data Privacy for PM2.5 Prediction in Smart City Sensing Applications

Concentrated Differentially Private Federated Learning With Performance Analysis

Contact Info

Product

Resources

About