Ofertes de projectes

TinyML is a growing community that does machine learning on microcontrollers https://www.tinyml.org. 

Microcontrollers are sometimes the only choice when the power supply is limited, e.g. in for battery- or solar-powered applications. Application examples of TinyML are "wildlife" observation, such as: https://mybirdbuddy.com/ https://opencollar.io/, but tiny machine learning is also in domestic, healthcare and industrial applications.

While Arduino Uno boards are well known for all kinds of hobbyist microcontroller projects, for machine learning the more powerful 32 bit microcontrollers and development boards are used, such as the Arduino Portenta H7 or Arduino Nano 33 BLE Sense. These are able to run machine learning applications. To get the first information on this topic have a look at TensorFlow Lite for Microcontrollers: https://www.tensorflow.org/lite/microcontrollers.

In this project we would like to explore on-device machine learning model training specifically doing it by federated learning. We would like to use the Arduino Portenta H7, the most powerful microcontroller board from the Arduino family. We have Arduino Portenta H7 that can be used for the project. 

We have done previous work and code available where we showed that it is feasible.

https://www.mdpi.com/2079-9292/11/4/573

In the project it would be interesting to explore the capacities of the Portant H7, like using the two cores of the board, use at least one the sensors (camera, accelaration, microphone,...), and use the network connectivity (Wifi, BLE, LoRa)...

One concrete case would be to use for the communication part of federated learning the Wifi connectivity of the Arduino Portenta H7 to build a "cluster" (inspired by clusters of Raspberry Pi) where all the nodes are interconnected and can participate in federated learning. 

The project can start with already working code (in C/C++, Python) that we have from previous work on TinyML with on-device training and federated learning. https://upcommons.upc.edu/bitstream/handle/2117/353756/160036.pdf

Remuneration: Possibility of making an Educational Cooperative Agreement (Conveni de Cooperació Educativa)

La imformació mñes detallada es comentarà personalment amb l'estudiant interessat .

Internet services are known to collect large amounts of personal information (PI). As a result, more than half of EU citizens are concerned about their online privacy. In this context, data brokers are companies devoted to collect and sell PI to other companies. Data brokers are often implemented as third-party trackers, which allow them to gain visibility across the Internet. Recent works show that collected PI is not only used for targeted advertising, but also for more obscure practices, such as price discrimination, background scanning, phishing or identity theft.

In this project, you will collaborate in the development of EPRIVO, the first European-wide online privacy observatory. EPRIVO will continuously scan the Internet, from multiple locations across Europe, in the search of third-party trackers that do not respect basic privacy rights and current EU regulations (GDPR 2016/679). Quantitative results will be published through an online service developed within the project. Such results will be useful to Internet users, policy makers, website owners and researchers.