Differentially Private Federated Learning for Resource-Constrained Internet of Things

by   Rui Hu, et al.

With the proliferation of smart devices having built-in sensors, Internet connectivity, and programmable computation capability in the era of Internet of things (IoT), tremendous data is being generated at the network edge. Federated learning is capable of analyzing the large amount of data from a distributed set of smart devices without requiring them to upload their data to a central place. However, the commonly-used federated learning algorithm is based on stochastic gradient descent (SGD) and not suitable for resource-constrained IoT environments due to its high communication resource requirement. Moreover, the privacy of sensitive data on smart devices has become a key concern and needs to be protected rigorously. This paper proposes a novel federated learning framework called DP-PASGD for training a machine learning model efficiently from the data stored across resource-constrained smart devices in IoT while guaranteeing differential privacy. The optimal schematic design of DP-PASGD that maximizes the learning performance while satisfying the limits on resource cost and privacy loss is formulated as an optimization problem, and an approximate solution method based on the convergence analysis of DP-PASGD is developed to solve the optimization problem efficiently. Numerical results based on real-world datasets verify the effectiveness of the proposed DP-PASGD scheme.


page 2

page 3

page 4

page 5

page 7

page 8

page 9

page 10


Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism

Recent years have witnessed a rapid proliferation of smart Internet of T...

Sparsified Privacy-Masking for Communication-Efficient and Privacy-Preserving Federated Learning

Federated learning has received significant interests recently due to it...

Considerations on the Theory of Training Models with Differential Privacy

In federated learning collaborative learning takes place by a set of cli...

Differentially Private Federated Learning via Inexact ADMM

Differential privacy (DP) techniques can be applied to the federated lea...

Blockchained Federated Learning for Internet of Things: A Comprehensive Survey

The demand for intelligent industries and smart services based on big da...

FedSel: Federated SGD under Local Differential Privacy with Top-k Dimension Selection

As massive data are produced from small gadgets, federated learning on m...

Federated Learning for Autoencoder-based Condition Monitoring in the Industrial Internet of Things

Enabled by the increasing availability of sensor data monitored from pro...

Please sign up or login with your details

Forgot password? Click here to reset