Anomalies detection in distributed networks and cyber-physical systems (CPS) is a challenging task and require multi-objective optimisation. In recent years, Federated machine learning is used for privacy-preserved machine learning. In Federated Learning many decentralised distributed computing nodes (edge or servers) train a local model on local data and then local model are combined centrally to generate a global model. There are different frameworks and algorithms to implement a Federated Learning system. In this project, the aim to design and implement an efficient Federated Machine Learning Algorithm for anomaly detection and prediction in IoT.
A. Feraudo, P. Yadav, V. Safronov, D. A. Popescu, R. Mortier, S. Wang, P. Bellavista, and J. Crowcroft:
Colearn: Enabling federated learning in mud compliant IoT edge networks. The 3rd International Workshop on Edge Systems, Analytics and Networking (EdgeSys20). New York: ACM, April 2020 [PDF]
Colby R. Banbury, Vijay Janapa Reddi, Max Lam, William Fu, Amin Fazel, Jeremy Holleman, Xinyuan Huang, Robert Hurtado, David Kanter, Anton Lokhmotov, David A. Patterson, Danilo Pau, Jae-sun Seo, Jeff Sieracki, Urmish Thakker, Marian Verhelst, Poonam Yadav:
Benchmarking TinyML Systems: Challenges and Direction. MLSys 2020 (CoRR abs/2003.04821)(2020)
Internet-of-things (IoT) enabled smart build environments are an accurate representation of complex and dynamical systems. The diversity and heterogeneity of components in the IoT, not only make the ecosystem extremely difficult to analyse and validate but also make it hard to build both secure and accountable. To address these issues, the Internet Engineering Task Force (IETF) has taken the initiative to bring a standard (RFC8520), which will encourage manufacturers of IoT devices to provide a Manufacturer Usage Description (MUD) for their IoT devices. In our work, we explore a deployment scenario of MUDs in domestic settings and proposing an MLogger application which runs on a local router. MLogger enforces user-defined traffic filtering policies along with the MUD policies for each IoT devices. Our solution not only aims to provide a better fine-grained traffic filtering locally but also enables a user-defined control and accountability at the edge of the network.
[New] Poonam Yadav, Angelo Feraudo, Budi Arief, Siamak F. Shahandashti Vassilios G. Vassilakis:
John Moore, Andrés Arcia-Moret, Poonam Yadav, Richard Mortier, Anthony Brown, Derek McAuley, Andy Crabtree, Chris Greenhalgh, Hamed Haddadi, Yousef Amar:
Zest: REST over ZeroMQ. PerCom Workshops 2019: 1015-1019 [PDF] [bibtex]
Diana Andreea Popescu, Vadim Safronov, Poonam Yadav, Roman Kolcun, Anna Maria Mandalari, Hamed Haddadi, Derek McAuley, Richard Mortier:
"Sensing" the IoT network: Ethical capture of domestic IoT network traffic: poster abstract. SenSys 2019: 406-407
Citizen Cyberlab: The central focus of the research project is understanding creativity and learning in on-line citizen science. To explore these aspects of citizen science, we are evaluating existing on-line collaborative environments and software tools to assess their role in supporting and stimulating creative learning, as well as examining the best practices of current Citizen Science projects.
The OpenShare platform is built to allow a group of collaborating entities to interact with media data for processing complex shared workflows. It will support provisioning of computational capacity to undertake these workflows on cloud computing infrastructure. OpenShare also provides necessary tools and services to control access to the data. It also ensures that both data and the tasks to be carried out on it are only available to authorised individuals or groups.
Recent years have seen an increase in diversity initiatives worldwide with different organisations emphasizing the need for a 50-50 male and female workforce distribution. Different initiatives have been proposed to bring women on boards, especially in STEM (Science, Technology, Engineering, Mathematics) and make them comfortable in the current working environments. To understand the impact of these initiatives, ACM-W UK conducted an online survey. This work presents the useful insights drawn from the results of the survey and also our recommendations for STEM and computing fields to increase female numbers in their programs.