Mobile Edge Computing, Blockchain and Reputation-based Crowdsourcing IoT Federated Learning: A Secure, Decentralized and Privacy-preserving System

By Yang Zhao, Jun Zhao, Linshan Jiang, Rui Tan, Dusist Niyato. 2019

113 citations recorded by the source corpus.

Internet-of-Things (IoT) companies strive to get feedback from users to improve their products and services. However, traditional surveys cannot reflect the actual conditions of customers' due to the limited questions. Besides, survey results are affected by various subjective factors. In contrast, the recorded usages of IoT devices reflect customers' behaviours more comprehensively and accurately. We design an intelligent system to help IoT device manufacturers to take advantage of customers' data and build a machine learning model to predict customers' requirements and possible consumption behaviours with federated learning (FL) technology. The FL consists of two stages: in the first stage, customers train the initial model using the phone and the edge computing server collaboratively. The mobile edge computing server's high computation power can assist customers' training locally. Customers first collect data from various IoT devices using phones, and then download and train the initial model with their data. During the training, customers first extract features using their mobiles, and then add the Laplacian noise to the extracted features based on differential privacy, a formal and popular notion to quantify privacy. After achieving the local model, customers sign on their models respectively and send them to the blockchain. We use the blockchain to replace the centralized aggregator which belongs to the third party in FL. In the second stage, miners calculate the averaged model using the collected models sent from customers. By the end of the crowdsourcing job, one of the miners, who is selected as the temporary leader, uploads the model to the blockchain. Besides, to attract more customers to participate in the crowdsourcing FL, we design an incentive mechanism, which awards participants with coins that can be used to purchase other services provided by the company.

Subfields: Artificial Intelligence, Computer Science Applications, Sociology and Political Science

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Science Applications

In Sociology and Political Science

Citations in the corpus

Federated Learning: Challenges, Methods, and Future Directions (2019, methodology): “Federated learning methods have been deployed by major service providers [11, 124], and play a critical role in supporting privacy-sensitive applications where the training data are distributed at the edge [e.g., 5, 46, 51, 89, 105, 127, 139].”
A Joint Learning and Communications Framework for Federated Learning Over Wireless Networks (2019, unknown): “01 W f 10(9) Ki [12,10,8,4,2] ς 10−27 γT 100 ms ωi 40 γE 0.”
Biscotti: A Blockchain System for Private and Secure Federated Learning (2021, background): “…work, eventual consistency, and ledger-based consensus, blockchains have been used in decentralized multi-party settings, such as currency management [31], [32], archival data storage [33], [34], ﬁnancial auditing [35], privacy-preserving computation [36] and IoT edge computation [37], [38], [39].”
Privacy-Preserving Solutions for Blockchain: Review and Challenges (2019, background): “For instance, in [128] authors employ Differential privacy to avoid an adversary can infer sensitive personal information when per-”
Federated Learning in Smart City Sensing: Challenges and Opportunities (2020, unknown): “The central server ﬁrst saves the initial data and distributes it to the participating users [125].”
Federated Learning for Healthcare Informatics (2019, background): “Other applications include smart retail [112] and finance [54].”
A Survey on Federated Learning: The Journey From Centralized to Distributed On-Site Learning and Beyond (2021, background): “Many aspects are considered in [73] to implement a fully secured FL approach for IoT.”
A Deep Blockchain Framework-Enabled Collaborative Intrusion Detection for Protecting IoT and Cloud Networks (2021, background): “Privacy-preserving approaches can be divided into four categories: 1) encryption based [12]; 2) differential privacy [13]; 3) perturbation based [13], [14]; and 4) blockchain-based mechanisms [15].”
Communication and Consensus Co-Design for Low-Latency and Reliable Industrial IoT Systems (2019, background): “nd its impact on consensus reliability and latency should be carefully examined. On this account, wireless distributed consensus protocols have recently been studied in several works [10], [11], [13]–[18]. For instance, a Hashgraph-motivated wireless distributed consensus protocol has been introduce”
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection (2019, background): “One related area with FL is edge computing [44], [127], [140], [195], [201], where the parties are edge devices.”
Privacy-Preserving Traffic Flow Prediction: A Federated Learning Approach (2020, background): “Because such models need to upload a large number of parameters to the cloud in the FL framework, it leads to expensive communication overhead which can cause training failures for a single model or a global model [41], [42].”