Federated learning and its combination with differential privacy is the latest technique for building privacy-aware machine-learning models . Its primary assumption – no data leaves the local data storage, has enabled its application in a variety of privacy-sensitive domains: mobile keyboard prediction , human mobility modeling based on GPS data , modeling from electronic health records  , etc.
Artificial Intelligence (AI) methods can bring significant and sustainable improvements to our lives. However, end-users must be able to understand those systems. Unfortunately, today’s groundbreaking AI methods are black-boxed (i.e., the decision model and the process are not understandable). The increased complexity of AI algorithms has made previous eXplainable AI (XAI) tools unsuitable, including the fact that most of the XAI solutions are not designed to operate under privacy constraints.
This project will investigate XAI techniques compatible with privacy-aware approaches (e.g., federated learning). The focus will be on counterfactual explainers  for wearable sensing data. Specific project tasks are:
1. Analyze XAI tools that can operate under privacy constraints, focusing on counterfactuals.
2. Pre-process one dataset from wearable sensing systems. Example datasets include emotion recognition, activity recognition and energy expenditure estimation [6, 7, 8].
3. Develop machine learning models for one of the datasets in step 2, and apply existing XAI tools on the models developed, including the method for generating counterfactual explanations, BayCon [10, 11].
4. Develop XAI tool for counterfactual explanations that can operate under privacy constraints.
This project is available as a MSc thesis and as a UROP internship project.
 J. Konečný, H. B. McMahan, F. X. Yu, P. Richtárik, A. Theertha Suresh, and D. Bacon. “Federated learning: Strategies for improving communication efficiency.” arXiv preprint arXiv:1610.05492 (2016).
 Andrew Hard, Kanishka Rao, Rajiv Mathews, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, and Daniel Ramage. Federated learning for mobile keyboard prediction. CoRR, abs/1811.03604, 2018. URL http://arxiv.org/abs/ 1811.03604.
 Ezequiel, C. E. J., Gjoreski, M., & Langheinrich, M. (2022). Federated Learning for Privacy-Aware Human Mobility Modeling. Frontiers in Artificial Intelligence, 5, 867046.
 Brisimi, T. S., Chen, R., Mela, T., Olshevsky, A., Paschalidis, I. C., & Shi, W. (2018). Federated learning of predictive models from federated electronic health records. International journal of medical informatics, 112, 59-67.
 Jiang, J. C., Kantarci, B., Oktug, S., & Soyata, T. (2020). Federated learning in smart city sensing: Challenges and opportunities. Sensors, 20(21), 6230.
 T. Miller, “Explanation in artificial intelligence: Insights from the social sciences,” Artificial intelligence 267: 1-38, 2019.
 M. Laporte, D. Gasparini, M. Gjoreski, and M. Langheinrich. “Exploring LAUREATE-the Longitudinal multimodAl stUdent expeRience datasEt for AffecT and mEmory research.” in the UbiComp/ISWC’22 Adjunct Proceedings, 2022.
 Gashi, S., Min, C., Montanari, A., Santini, S., & Kawsar, F. (2022). A multidevice and multimodal dataset for human energy expenditure estimation using wearable devices. Scientific Data, 9(1), 1-14.
 Gjoreski, M., Kiprijanovska, I., Stankoski, S., Mavridou, I., Broulidakis, M. J., Gjoreski, H., & Nduka, C. (2022). Facial EMG sensing for monitoring affect using a wearable device. Scientific reports.
 M. Gjoreski M, V. Kuzmanovski V, and M. Bohanec “BAG-DSM: A Method for Generating Alternatives for Hierarchical Multi-Attribute Decision Models Using Bayesian Optimization,” Algorithms, 2022.
 P. Romashov, M. Gjoreski, K. Sokol, M. V. Martinez, and M. Langheinrich. “BayCon: Model-agnostic Bayesian Counterfactual Generator,”. In IJCAI 2022.
For more information contact: Martin Gjoreski